{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy.linalg as la\n",
    "import matplotlib.pyplot as plt\n",
    "from testsuite import generate_state_space, get_distribution, generate_iid_samples\n",
    "%precision 4\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DREAM Implementation\n",
    "\n",
    "**Source**: *Vrugt, J. A. (2016). Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation. Environmental Modelling & Software, 75, 273-316.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-defined parameters and fucntions\n",
    "\n",
    "It is necessary to pre-define some parameters and functions for the DREAM algorithm, in which those components strictly follow the implementation methods in the original paper.\n",
    "\n",
    "### Main parameters\n",
    "\n",
    "| Parameter | Initial Value | Definition | \n",
    "| ---- | ---- | ---- |\n",
    "| $N$ | 5000 |  number of steps |\n",
    "| $D$ | 2 | dimension | \n",
    "| $P$ | 10 | number of chains|\n",
    "| $n_{CR}$ | 3 | number of Crossover Rate (CR) values |\n",
    "| $\\delta$ | 3 | maximum number of chain pairs used to generate the jump |\n",
    "\n",
    "### Auxiliary parameters\n",
    "\n",
    "| Parameter | Initial Value | Definition | \n",
    "| ---- | ---- | ---- |\n",
    "| $c$ | 0.1 | $e \\in \\mathcal{U}_d(-c, c)$ with $c$ small | \n",
    "| $c^*$ | $10^{-12}$ | $\\varepsilon_d \\in \\mathcal{N}_d(0, c^*)$ with $c^*$ small |\n",
    "| $p_g$ | 0.2 | selection weight for $\\gamma$ (choosing $1$ or $\\gamma = \\sqrt{2.38\\delta^* d}$) |\n",
    "| $CR$ | \\[1/3, 2/3, 3/3\\] | CR values | \n",
    "| $\\Delta$ | \\[0, 0, 0\\] | the squared normalized jump distance for each CR value | \n",
    "| $f_{CR}$ | \\[0, 0, 0\\] | an $n_{CR}$-vector to record the frequency of each CR value being selected | \n",
    "| $p_{CR}$ | \\[1/3, 1/3, 1/3\\] | probabilities for CR values | "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta, c, c_star, n_CR, p_g = 3, 0.1, 1e-12, 3, 0.2\n",
    "Delta = np.zeros(shape=(1, n_CR))\n",
    "f_CR = np.zeros(shape=(1, n_CR))\n",
    "CR = np.arange(start=1, stop=n_CR+1)/n_CR\n",
    "p_CR = np.ones(shape=(1, n_CR))/n_CR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial state and probability density function (pdf)\n",
    "\n",
    "To run the DREAM algorithm, the state and its probability density function should be introduced in advance. \n",
    "\n",
    "In the paper, $\\mathbf X$ is the population state, which contains $P$ $d$-dimensional points. And $p_X$ stores the density of $\\mathbf X$. Similarly, $\\mathbf x$ denotes the whole populations, in which there are $N$ rows (steps) and $P$ $d$-dimensional points in each row. Besides, $p_x$ is the density of $\\mathbf x$.\n",
    "\n",
    "| Denotation | Meaning | Size |\n",
    "| ---- | ---- | ---- | \n",
    "| $\\mathbf X$ | population state | $P \\times D$ | \n",
    "| $p_X$ | current population density | $P \\times 1$ | \n",
    "| $\\mathbf x$ | all the chains | $N \\times D \\times P$ | \n",
    "| $p_x$ | all the densities | $N\\times P$ |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_initial_population(nb_chains, dim):\n",
    "    return np.array(np.random.uniform(low=-10, high=10, size=nb_chains*dim).reshape((nb_chains, dim)))\n",
    "\n",
    "def generate_initial_density(nb_chains):\n",
    "    return np.array(np.zeros(shape=(nb_chains, 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Append* from current state to the population\n",
    "\n",
    "After updating the current state, instant results append to the whole population. Remember that $\\mathbf X$ is a $P \\times D$ matrix, and $\\mathbf x$ is an $N \\times D \\times P$ matrix. \n",
    "\n",
    "To implement this operation in Python, we have to transpose the $\\mathbf X$ to a $D \\times P$ matrix and then append it to the position $n$ in $\\mathbf x$. This is the same to appending the density. We first transpose the $p_X$ to an $1 \\times P$ matrix, then append it to the position $n$ in $p_x$. Here, $n$ is the step number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def append_chain(chains, current_chains, dim, nb_chains, n):\n",
    "    chains[n] = np.reshape(np.transpose(current_chains), newshape=(dim, nb_chains))\n",
    "    return chains\n",
    "\n",
    "def append_density(density, current_density, n):\n",
    "    density[n] = np.transpose(current_density)\n",
    "    return density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize the state and the density\n",
    "\n",
    "The current state $\\mathbf X$ and the density of the current state $p_X$ will be generated at the beginning. The DREAM algorithm then appends them to the population $\\mathbf x$ and the whole density $p_x$, respectively.  \n",
    "\n",
    "Note that, the process above is regarded as the first loop. Consequently, the step number in the main DREAM process is from $1$ to $N-1$, instead of $0$ to $N-1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_pdf(current_chains, nb_chains):\n",
    "    current_density = generate_initial_density(nb_chains=nb_chains)\n",
    "    for i in range(nb_chains):\n",
    "        current_density[i] = pdf(current_chains[i, :])\n",
    "    return current_density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_chains(nb_steps, nb_chains, dim, pdf):\n",
    "    initial_state = generate_initial_population(nb_chains=nb_chains, dim=dim)\n",
    "    initial_density = calculate_pdf(initial_state, nb_chains)\n",
    "    population = np.zeros(shape=(nb_steps, dim, nb_chains))\n",
    "    density = np.zeros(shape=(nb_steps, nb_chains))\n",
    "    population = append_chain(chains=population, current_chains=initial_state, dim=dim, nb_chains=nb_chains, n=0)\n",
    "    density = append_density(density=density, current_density=initial_density, n=0)\n",
    "    return initial_state, initial_density, population, density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Propose the pre-candidate\n",
    "A pre-candidate $\\mathbf x_d^*$ is generated as shown in Equation $1$ below.\n",
    "\n",
    "$$\\mathbf x_d^* = \\mathbf x_n^{(p)} + (1+e)I_d \\sigma \\sum_{j=1}^{\\delta^*} (\\mathbf x_n^{(a_j)}-\\mathbf x_n^{(b_j)}) + \\varepsilon_d \\tag{1}$$\n",
    "where $\\delta^* \\in \\{1, 2, \\dots, \\delta\\}$ signifies the number of pairs used to make differences for generating the candidate. $e \\in \\mathcal{U}_d(-c, c)$ and $\\varepsilon_d \\in \\mathcal{N}_d(0, c^*)$ with $c$ and $c^*$ small compared to the width of the target distribution.\n",
    "\n",
    "### Generate two index arrays\n",
    "$\\sum_{j=1}^{\\delta^*} (x_n^{(a_j)}-x_n^{(b_j)})$ is the sum of $\\delta^*$ different chains in the population state. $a_j$ and $b_j$ both store $\\delta^*$ indices of points in the population state. Those indices are different with each other and not equal to $p$. To achieve that, $2 \\delta^*$ indices are selected from integers $0$ to $P-1$. If an index is equal to the index of chain for update, we sample once again until the requirements are well met. Otherwise, those indices are assigned to two arrays as $a_j$ and $b_j$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_two_random_arrays(nb_chains, selected_delta, index_of_chain):\n",
    "    array_candidate = np.random.choice(range(nb_chains), size=2*selected_delta, replace=False)\n",
    "    for k in range(len(array_candidate)):\n",
    "        if array_candidate[k] == index_of_chain:\n",
    "            generate_two_random_arrays(nb_chains, selected_delta, index_of_chain)\n",
    "        else:\n",
    "            return array_candidate[: selected_delta], array_candidate[selected_delta :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sum of the differences\n",
    "Calculate the sum of differences of pairs of chains as $\\sum_{j=1}^{\\delta^*} (x_n^{(a_j)}-x_n^{(b_j)})$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_difference(current_chains, selected_delta, index_of_chain, nb_chains):\n",
    "    a, b = generate_two_random_arrays(nb_chains, selected_delta, index_of_chain)\n",
    "    total_sum = 0\n",
    "    for j in range(len(a)):\n",
    "        temp = 0\n",
    "        temp = current_chains[a[j]] - current_chains[b[j]]\n",
    "        total_sum = total_sum + temp\n",
    "    return total_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate the pre-candidate $\\mathbf x_d^*$\n",
    "$\\sigma$ is calculated by $\\sigma(\\delta^*, d) = 2.38/\\sqrt{2\\delta^*d}$. And a mix probability $p_g$ is used to select between $\\sigma$ and $1$. Finally, the pre-candidate is well generated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_candidate(chain, population_state, delta, index_of_chain, dim, nb_chains):\n",
    "    delta_star = int(np.random.choice(range(delta), size = 1)) + 1\n",
    "    sigma_candidate = 2.38/np.sqrt(2*delta_star*dim)\n",
    "    sigma = np.random.choice([sigma_candidate, 1], size = 1, replace = 'true', p = [1-p_g, p_g])\n",
    "    return chain + (1 + np.random.uniform(-c, c, 1)) * sigma * calculate_difference(population_state, delta_star, index_of_chain, nb_chains) + c_star * np.random.normal(size = D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsapce sampling\n",
    "\n",
    "Suppose we consider a $d$-dimensional chain in the current state. Only selected dimensions are going to be updated. And the indices of those dimensions will be stored in the subspace set $A$.\n",
    "\n",
    "Technically,  one index called $idx$ is randomly selected in the range of integers 1 to $n_{CR}$ (number of crossover values).  Then, $d$ values are stored in $\\mu$, which are randomly selected in the range from $0$ to $1$. Note that those $d$ values are sorted, and their subscripts are used to represent indices of dimensions. Finally, we pick up all the values that less than the $idx$th element in CR values. Their subscripts are the dimensions going to be updated. In case that there is no dimension being selected, a random dimension is assigned to the subspace set $A$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_subspace(n_CR, p_CR, CR, dim):\n",
    "    idx = np.random.choice(range(n_CR), size=1, p=p_CR[0])\n",
    "    mu = np.random.uniform(low=0, high=1, size=dim)\n",
    "    subspace = list(np.where(mu < CR[idx]))[0]\n",
    "    if len(subspace) == 0:\n",
    "        subspace = np.random.choice(dim, size=1)\n",
    "    return subspace, idx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make up candidate $\\mathbf x^*$\n",
    "\n",
    "This step is to combine components from the chain going to update $x_n^{(p)}$ and the full candidate $x_d^*$. The components of the chain in subspace are replaced by those of the full candidate.\n",
    "\n",
    "In the documentation, we use matrix operation as Equation .\n",
    "\n",
    "$$\n",
    "x^* = [S_{\\bar{A}} \\ \\ S_A]\\ \n",
    "\t\t\\left[ \\begin{array}{c}\n",
    "\t\tx_n^{(p)} \\\\ x_d^*\n",
    "\t\\end{array} \\right]\n",
    "$$\n",
    "where the $i$th diagonal component in $S_A$ is $1$, if $i \\in A$; $0$ otherwise. And the $i$th diagonal component in $S_{\\bar{A}}$ is $0$, if $i \\in A$; $1$ otherwise.\n",
    "\n",
    "Here, we use a more straightforward way to implement the idea above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_up_candidate(subspace, chain, pre_candidate, dim):\n",
    "    candidate = []\n",
    "    for i in range(dim):\n",
    "        if i in subspace:\n",
    "            candidate.append(pre_candidate[i])\n",
    "        else:\n",
    "            candidate.append(chain[i])\n",
    "    return candidate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accept or reject the proposal\n",
    "\n",
    "### Metropolis-Hastings algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MH_acceptance_ratio(current, candidate, pdf):\n",
    "    p_current, p_candidate = pdf(current), pdf(candidate)\n",
    "    return p_candidate/p_current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accept_p(acceptance_ratio):\n",
    "    return np.random.rand() < acceptance_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update the point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_point(point, chains, n_CR, p_CR, CR, delta, index_of_chain, dim, nb_chains):\n",
    "    subspace, idx = generate_subspace(n_CR, p_CR, CR, dim)\n",
    "    pre_candidate = generate_candidate(point, chains, delta, index_of_chain, dim, nb_chains)\n",
    "    candidate = make_up_candidate(subspace, point, pre_candidate, dim)\n",
    "    acceptance_ratio = min(1, MH_acceptance_ratio(point, candidate, pdf))\n",
    "    if accept_p(acceptance_ratio):\n",
    "        return candidate, idx, pdf(candidate), candidate - point\n",
    "    else:\n",
    "        return point, idx, pdf(point), np.zeros(shape=(dim))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Update the CR PMF\n",
    "To update the CR PMF, we introduce a measurement named _squared normalized jumping distance_.\n",
    "\n",
    "$$\\Delta_{i} \\leftarrow \\Delta_{i} + \\sum_{k=1}^d \\left( \\frac{X_{n,k}^{(p)}-X_{n-1,k}^{(p)}}{r_j} \\right)^2 \\tag{2}$$\n",
    "where $r_j$ is the *standard deviation* of the last state $\\mathbf X_{n-1, j}^{(p)}$. $\\Delta$ then stores the squared normalized jumping distance of a full-dimension jump for _each index of CR values_. The total squared normalized jumping distances in each CR value are sequently stored in $\\Delta$ after a chain evolution. It evaluates _the distance of the point jumps_ under each CR value. \n",
    "\n",
    "The frequency that a CR value is selected in a chain evolution are also recorded simultaneously and are stored in a list $n$.\n",
    "\n",
    "$$f_{CR, i} \\leftarrow f_{CR, i} + 1 \\tag{3}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_parameters_for_update_crossover(idx, std_dev, distance):\n",
    "    Delta[0][idx] = Delta[0][idx] + sum((distance/std_dev)**2)\n",
    "    f_CR[0][idx] = f_CR[0][idx] + 1\n",
    "    return Delta, f_CR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After a chain evolution, we calculate the new selection probability as follows:\n",
    "\n",
    "$$p_{CR} \\leftarrow \\frac{\\Delta}{n} \\tag{4}$$\n",
    "\n",
    "$$p_{CR} \\leftarrow \\frac{p_{CR}}{\\sum p_{CR}} \\tag{5}$$\n",
    "\n",
    "Equation 4 calculates the average squared normalized jumping distance for each CR value. In other words, the distance that the point jumps averagely once the certain CR value is selected. Large jumps are tend to be selected, so _the probability to selecting the corresponding CR value should be enlarged_. But the sum of the $p_{CR}$ now might not be 1, Equation 5 is thus used for standardization. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_CR_PMF(Delta, f_CR):\n",
    "    return (Delta/f_CR) / np.sum(Delta/f_CR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chain evolution\n",
    "A chain evolution is defined as all the chains in the current state have finished updating their states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chain_evolution(chains, std_X, n_CR, p_CR, CR, delta, dim, nb_chains):\n",
    "    for i in range(P):\n",
    "        chains[i], idx, pdf_of_population_state, distance = update_point(chains[i], chains, n_CR, p_CR, CR, delta, i, dim, nb_chains)\n",
    "        Delta, f_CR = generate_parameters_for_update_crossover(idx, std_X, distance)\n",
    "    return chains, Delta, f_CR, pdf_of_population_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correction for outlier chains\n",
    "After each chain evolution, the check for location of the chains is important, because the outlier chains will _lead the results to a situation where each chain is out of target distribution and slow down the speed of convergence_.\n",
    "\n",
    "To solve this problem, we introduce **Inter-Quartile-Range** (**IQR**), a measure of statistical dispersion. It is equal to the difference between 75th and 25th percentiles, $IQR = Q_3 - Q_1$. The IQR is a measure of variability,  based on dividing a data set into quartiles. Quartiles divide a rank-ordered data set into four equal parts. The values that separate parts are called the first, second, and third quartiles; and they are denoted by $Q_1$, $Q_2$ and $Q_3$, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_log(pdf_of_chains, n):\n",
    "    return np.mean(np.log(pdf_of_chains[int(n/2):n]), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iqr_cal(mean_log_mat):\n",
    "    q3, q1 = np.percentile(mean_log_mat, [75, 25])\n",
    "    iqr = q3 - q1\n",
    "    return q1 - 2*iqr #Here, the original paper uses 2*iqr to calculate the outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on theory mentioned above, We can illustrate Outlier chains handling method in DREAM. DREAM stores in $\\Omega$ _the mean of the logarithm of the posterior densities of the last 50% of the samples in each chain_. Through the IQR statistic, IQR is calculated. And chains with $\\Omega < Q_1 - 2IQR$ are considered outliers, and are _moved to the current best member of $\\mathbf X$_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check(population_state, pdf_of_whole_population, pdf_of_population_state, step, nb_chains):\n",
    "    mean_log_mat = mean_log(pdf_of_whole_population, step)\n",
    "    thres = iqr_cal(mean_log_mat)\n",
    "    replace = np.where(np.max(mean_log_mat))\n",
    "    MOVE = np.where(mean_log_mat < thres)\n",
    "    if len(MOVE) == 0:\n",
    "        return population_state, pdf_of_population_state\n",
    "    else:\n",
    "        for i in MOVE:\n",
    "            population_state[i] = population_state[replace]\n",
    "        pdf_of_population_state = calculate_pdf(population_state, nb_chains)\n",
    "        return population_state, pdf_of_population_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standard_deviation(current_chains):\n",
    "    return np.std(current_chains, axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gelman-Rubin Diagnostic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gelman_rubin_diagnostic(chains):\n",
    "    #calculate within chain varinace\n",
    "    W = np.zeros(shape = (D))\n",
    "    for i in range(P):\n",
    "        for j in range(D):\n",
    "            temp = np.std(chains[:, j, i])**2\n",
    "            W[j] = W[j] + temp\n",
    "    W = W/P\n",
    "    \n",
    "    #calculate between chain variance\n",
    "    mean_collect = np.zeros(shape = (P, D))\n",
    "    mean_mat = np.zeros(shape = (D))\n",
    "    for i in range(P):\n",
    "        for j in range(D):\n",
    "            mean_collect[i, j] = np.mean(chains[:, j, i])\n",
    "    for k in range(D):\n",
    "        mean_mat[k] = np.mean(mean_collect[:, k])\n",
    "    B_component = np.zeros(shape = (P, D))\n",
    "    for p in range(P):\n",
    "        for q in range(D):\n",
    "            B_component[p, q] = (mean_collect[p, q] - mean_mat[q])**2\n",
    "    B = np.zeros(shape = D)\n",
    "    for d in range(D):\n",
    "        B[d] = np.sum(B_component[:, d])\n",
    "    B = N*B/P\n",
    "    \n",
    "    var_theta = (1 - 1/(2*N)) * W + 1/(2*N)*B\n",
    "    return np.sqrt(var_theta/W)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Single DREAM Experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "N, P, D = 5000, 10, 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate target distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Available values for 'target_name': \"Pi_3\", \"Pi_4\"\n",
    "target_name = \"Pi_3\"\n",
    "SP = generate_state_space(dim=D, N=N)\n",
    "TARGET = SP['Test Suite'][target_name]\n",
    "pdf = TARGET['pdf']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main DREAM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dream(population_state, pdf_of_pop_state,  whole_population, pdf_of_whole_pop, pdf, p_CR, dim, nb_chains):\n",
    "    for n in range(1, N):\n",
    "        std_X = standard_deviation(population_state)\n",
    "        population_state, Delta, f_CR, pdf_of_pop_state = chain_evolution(population_state, std_X, n_CR, p_CR, CR, delta, dim, nb_chains)\n",
    "        whole_population = append_chain(whole_population, population_state, dim, nb_chains, n)\n",
    "        p_x = append_density(pdf_of_whole_pop, pdf_of_pop_state, n)\n",
    "        if n < N/10:\n",
    "            p_CR = update_CR_PMF(Delta, f_CR)\n",
    "        population_state, pdf_of_pop_state = check(population_state, pdf_of_whole_pop, pdf_of_pop_state, n, nb_chains)\n",
    "    return whole_population, pdf_of_whole_pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-time run DREAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_dream(nb_step, nb_chains, dim, pdf):\n",
    "    X, p_X, x, p_x = initialize_chains(nb_step, nb_chains, dim, pdf)\n",
    "    samples, pdf_samples = dream(X, p_X, x, p_x, pdf, p_CR, dim, nb_chains)\n",
    "    return samples, pdf_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Samples generated and their pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples, pdf_samples = run_dream(N, P, D, pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gelman-Rubin diagnostic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "gr_diag = gelman_rubin_diagnostic(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.0008, 1.0007])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr_diag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot samples "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(chain, nb_chain, target_name):\n",
    "    plt.figure(dpi = 600)\n",
    "    n = 256\n",
    "    x = np.linspace(-35, 35, n)\n",
    "    y = np.linspace(-100, 20, n)\n",
    "    X, Y = np.meshgrid(x, y)\n",
    "    plt.contour(X,Y, SP['Test Suite'][target_name]['Contour Function'](X,Y), levels =SP['Test Suite'][target_name]['Contour Levels'], colors='k', alpha=0.4, linewidth = 0.1)\n",
    "    plt.xlim(-40, 40)\n",
    "    plt.ylim(-30, 10)\n",
    "    plt.xlabel(r'$x_1$')\n",
    "    plt.ylabel(r'$x_2$')\n",
    "    plt.scatter(chain[int(N/2):N, 0, nb_chain], chain[int(N/2):N, 1, nb_chain], color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/matplotlib/contour.py:1000: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  s)\n"
     ]
    },
    {
     "data": {
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jvvwokevACqmqwzK5hWaa7Z6fyluA73eHgfHtPifg7AJYF7+V5FqbjD2juz+8yM1sQubuEpp3AJi7qrpdkvtvMvyuLPmKwO9xvYHxfxt5/Q8NjPvQAixEVd0kyZWmlHw9kxtstkveAlxg6ODsnCSf2cH8MhdgcWQusCzyB2C9yHWAC1wv+3+r+b4+uYO5p5G3wJ5SVVdI8hNTSs5J8oZtTi9zgV2vqq6V5FGbDJ+c5AmL281UMneX0LwDwFxV1cFJnrfJ8PlXBJ63wC0NudrA+Ekjrz90qDi0P4Adq6oDkvzRQNkLdvimMXkLcIE7D4z/R3efvYP5rz4wLnMB5kfmAssifwDWi1wHuMDQ+elp2f7Lj+QtwHc7PsmBU8Zf2d3/tc25ZS6wq208T/XCJBfepOSXu/uMBW5pGpm7S2jeAWDenpTkKpuMPaO7x+7gnVlVXSbJRQfK/nPkbQzNf+WR1wdIkqcnudGU8dOTPHO7k8tbgAtU1eEZ/vL5LTtc5qiBcZkLMD8yF1gW+QOwXoZyc9m5fkhV/eDIewA4/wHJew+UvW0HLwyVtwAbquoRSX5qSsk5SZ66gyWcXQC73UOS3GSTsb/u7hMXuZkBMneX0LwDwNxU1Q2SPHyT4ZOzOlcEnu9KA+PnJvniyHv4/MD4lUZeH9jDquqQqnpBkkcMlD5iB2/TSeQtwL5+LsmhAzX/uN3JZ2yY3Emmz2Iocw+uqkuPvAeA0clcYFnkD8B6qarK8EM2Y+f6F5IMPQjvQRtgEe6e4bzZ1vmpvAWYqKoLVdUTkvzhQOn/7e4PbnMNZxfArlZVV0jylE2Gv5HhZ60WRubuLpp3AJiLqjookysCN7tK9SErdEXg+S47MP7lHbyxZ1b/PTB+cFUdMfIegD2mqg6uqvsl+ViSBw2U/1l3/9kOl5S3AEmq6rAkjxoo+2KSN+xgmaHMTYYzcadmmX+WfQKsOpkLLIv8AVgvF0/yAwM1o+Z6d5+b5CsDZXIdGFVVHZjkiQNl30nyN9tcQt4Ce9pG087dk3wwye8MlL82yZN2sJyzC2C3e06SwzYZe0x3f2GRmxkgc3eRg5a9AQDWxq8lud4mY3/T3a9e5GZmdMmB8S8tYA+zrHHJJKeOvRFg/Ww0Vh6+8eeoTHL6xknums1/wdzXizPc3DMLeQsw8dtJht4m88yNL3C3ayhzT+vus3Yw/6Du/nZVfTPTbxga2ifAbiBzgWWRPwDrZZa8XNQZ6rRzC7kOjO3BSa4zUPOS7v7aNueXt8CesNEMeVgmzwlcLpPnBG6Qye1ml5phitcluUd3n72DbTi7AHatqvqfSe6yyfC/JnneArczC5m7i2jeAWDHquqHkzxuk+FvJHn4ArezFZcYGD9tAXuYZY1LJPnk2BsBdp+qum2S148w9RlJfq27nzun+eQtsOdV1Y2SPHKg7GtJnr3DpVYhc89fZ9rB3NA+AXYDmQssi/wBWC+z5OUqnKHKdWA0VXVUkqcOlJ2d5Pd2sIy8BdbCxnNaJ40w9TlJnpLkSTt80Vzi7ALYparqEkn+aJPhc5L8Qneft8AtzULm7iIHLHsDAKyFF2Tz66VX7YrAfV18YHz0Dy0bb6k4c6DMhxZgUc5J8tIk/2OOjTuJvAX2uKo6JJN8PXCg9Le7+xs7XG7pmTvjOjIXWAcyF1gW+QOwXoZy/Yw5PDw5C7kOLEVVHZDkxZncEjHN8d29k5ewyVuA/esk/5jkRt39+DllobMLYLd6Rja/JfEPu/vfFrmZGcncXcTNOwDsSFX9fJJbbTK8ilcE7muzhqPznbGQXSTfyvS9DO0TYKc+kOTvkvx5d588wvzyFtjrnpvkmgM170/y/DmstUqZO43MBdaBzAWWRf4ArBe5Dux1T0hyzEDNZ5M8aYfryFuA7/YfmTwn8LLu/sic55a5wK5TVbdN8oBNhk9J8vjF7WZLZO4u4uYdALatqi6T5GmbDJ+T5MEreEXgvi48MH7OQnYxvM7QPgF24ttJPpXk8xv/fQzyFtizquoXk9xvoOysJPef05vMZC7A4shcYFnkD8B6kevAnlVVd0rymIGyTvJz3X36DpeTtwAXOCfJJ5N8LsMPW2+HzAV2lao6ONNftvmQ7l5UE8xWydxdRPMOADvxx0mO2GTs+O7+0CI3sw0+tAAkF03y00n+NMlnq+rFVXXtOa8hb4E9qapuleSZM5Q+Zo5vNJO5AIsjc4FlkT8A60WuA3tSVV0rycsz/PzaH3f36+ewpLwFuMBBSe6UybNfn6yqv62qH5/j/DIX2G2emOQqm4y9ortPWORmtkjm7iKadwDYlqq6eyYPe+/PKl8RuK+hvwfn8ebzWQytc+BCdgGQXCiT618/XFXHV9W8riuVt8CeU1VXS/KKTLJ1mn/o7mfMcWmZC7A4MhdYFvkDsF7kOrDnVNUPJvmnJIcPlL4nya/NaVl5C7B/ByT5ySTvrKr/r6ouPqc5p5G5wMqoqhskecQmw6clefgCt7MdMncXOWjZGwBYdVX1w0netex9zFN3X2onP19Vhyd59pSSh3T3GFeqzttQp++i/p4cWufshewClkzebstHkzxoyvghmdyQdkSSKye5cZIjZ5i3MvnF89iqunt3f2KH+5S3sGJk7riq6oeSvCbJJQZKP5nkuDkvL3NhxcjctSZzgWWRPwDrRa4De0pVHZLkVdn8zebn+2qSe3X3d+a0tLwF1sWXMv05gYvmgucErpDJcwJHzTj3zyS5RVXdq7vfuYM9ylxgV6iqg5K8MJs3ljymu/9rgVvaDpm7i2jeARh2UJJLLnsTK+b3k1xuk7FVvyJwX0OHfIv6e3LobezzOoyEVSdvt2jjl8MXbuVnquoKSe6d5BeSXH2g/JpJ3lxVt+ru/9jeLpPIW1hFMnckVXWxTBp3hr54/nKSO3X3qXPegsyF1SNz15fMBZZF/gCsF7kO7BlVdeEkr8zkQfJpvp3k7t19yhyXl7fAWuju07L15wR+MJPbdR6c5OiB8ssleW1V3bG73769XcpcYNf4tSTX22Ts3Umeu8C9bJfM3UWGrkkCgO9SVTfP5IHv/dkNVwTua6iT98IL2YUPLcACdfdnu/vpmTTm3CvJFwd+5Mgkb9q4RWK75C2wJ1TVwZm8MXKzw73znZ5J485ObzbbH5kLsDgyF1gW+QOwXuQ6sCdU1QFJXpbk2IHSszO5cWe7D4xPm3caeQusre7+cne/oLtvkOTWST458COHJXlNVV1rm0vKXGDlVdUPJ/mdTYbPSfLg7j5vgVvaLpm7i2jeAWBmVXWRJC9IUpuU/NYuuCJwX98cGD9sIbsYXmdonwBb1hOvSHLtJK8eKD8yW3xzz/eQt8Da2+eNkT8xUHpmkrt293tH2sqqZO7hA+MyF1gHMhdYFvkDsF6G8vLQhexCrgMjqqrK5FmDew2UnpfkAd19wgjbkLcASbr7TUn+R5I/Gyg9NMnLqmroYez9cXYB7AYvSHLRTcb+qLs/uMjN7IDM3UU07wCwFY/N5KaG/Xl3kucscC/z8LWB8dE/tGy8nf3AgbKhfQJsW3d/NZPrsV87UHqXqrrvNpeRt8Baq6oDk7w8yR0GSs9Ocs/ufsuI21l65s64jswF1oHMBZZF/gCsl6G8vFBV/cAC9iHXgTH9YZKfm6Hul7r75SPtQd4CbOjuM5L8fIYbeK6f5De3sYSzC2ClVdXPJbnVJsOnJHncArezUzJ3F9G8A8BMquo62fyXsXOze64I3NdXB8aPWMAeZlljaJ8AO9Ld38mkgec/Bkoftc0l5C2wtjbeGPmiJD81UHpukv810hsj97UKmZskFxsYl7nAOpC5wLLIH4D1MktersIZqlwHtqWqnpzk4TOU/mp3v2DErchbgH10dyd5UJI3D5Q+vKo2u5liM84ugJVVVT+U5GlTSh7a3d9a1H7mQObuIpp3ABhUVQckeWGSza5BPX4XXRG4r68MjF9mAXs4coYaH1qA0XX3t5P8n4Gya1fVsduYXt4C6+w5Se43UNNJfr67/2YB+xnK3ItU1aiHc1V1ySQXHiiTucA6kLnAssgfgPUylOvJapyhynVgy6rqN5P81gylj+vuZ4y8HXkL8D02XtT8sExeQreZSyW5/xandnYBrLI/TnLxTcZe2d2vWuRm5kDm7iIHLXsDAKuuuz+epJa9jyV7WJIf22TsM9ldVwTu6zMD4xerqh/o7jNH3MMPDYx/eeOBelh78nb5uvvEqnpHkptOKbtbktducWp5CytG5s5HVT09yS/OUPrQ7n7xyNs531DmJpNMPHXEPQxlbjLbPmEtyNy1JnOBZZE/AGuku8+oqq8mueSUsllyd9uq6uAkhw6UnTLmHoD1U1W/kuSpM5Q+rbufOPZ+5C3A/nX3v1fVXyX52Slld0vy/C1M6+wCWElVdbck99xk+LQkv7LA7cyLzN1F3LwDwFQb154+eUrJbrsicF8nz1BzxZH3cNTA+KdHXh/ge/39wPgttzHnyTPUyFtgV6mqJyd55Aylv97dzxl7P+fr7m9m+I01Q5m4U0Pzf2kX/w4B8P/IXGBZ5A/AWjp5YHzZuZ7Mds4LkCSpqgclOX6G0md392+MvZ99nDwwLm+BvWroOYGfqKqZnzd2dgGssGm3PT62u/9rYTuZE5m7u7h5B4AhF8nmb345JckPVdXPz3G9qw2MHz3Deid09xeGFurub1bVVzK53nXafj4xNNcODP37epgcWLQTkvz+lPFrVdUhW/mFSt4C66aqfivJb81Q+rju/oOx97Mfn870t0deLcnrRlxf5gJ7icwFlkX+AKyXTye5wZTxodzdqaH5v9jdZ4y8B2BNVNX9kjwvwzcR/2mSh42/o+8ibwH27zVJzsvmFwIcnuQaST62hTmdXQCraLNnl05Lctacn4U9emD8ajOs95buPmmGtWTuLqF5B4CdOCrJnyx4zbtu/JnmVkkGm3c2fCTTb5G4RiYPso/l6gPjHxlxbYD9GfplqTL5RXarb0OQt8BaqKpHZvrNlOf7ve5+4tj72cRHktxwyvg1Rl5f5gJ7icwFlkX+AKyXjyS555RxuQ7sClV1ryQvyuYPf5/v5Ul+obt7/F19F3kLsB/dffrGCzkvPaXs0tla846zC2A3OTzJ8xe85k03/kzzwCSzNO/I3F1i5mvsAGBNvX9g/Pojrz/UXf2BkdcH+C7d/e0kpw+UTXtTw2bkLbDrVdUvJXn6DKXP6u5Hjb2fKWQuwOLIXGBZ5A/AepHrwK5XVXdL8hdJDhwo/fsk9+/u88bf1feRtwCb++LA+FafE5C5AIsjc3cJzTsA7HVDH1qmXZm9I1V1ZJIjB8p8aAGW4bSB8YtuY055C+xqVfXAJM+eofSFSR4+8naGDGXu9apq6Av0bamqg5L86ECZzAXWicwFlkX+AKyXoVy/fFVNewv6Tg2dz8p1YKqqOjbJXye50EDpiUnu093njL+r/ZK3AJub93MCzi4AFkfm7hKadwDY6946MH7Nqvqhkda+1cD4yd39+ZHWBpjmkIHxb21jTnkL7FpV9TOZNOXUQOlfJHlwd/f4u5rqvUnOnDJ+aMZrmrxxkoOnjJ+Z5H0jrQ2wDDIXWBb5A7BGuvtzSU4ZKDtmjLWr6rJJrj5Q9rYx1gbWQ1Udk+TvklxkoPSfk/xUd39n9E1tQt4CTDXv5wScXQAsjszdJTTvALCndfcpST45paSS3Hak5Yfmff1I6wJsqqoOTXLEQNnXtzqvvAV2q6r6ySQvzfAZyiuSPKC7zxt/V9N195lJ3j5QdruRlh/K3Ldu7A9gLchcYFnkD8BaesPA+LJy/aSN812A71NVN0nyTxm+jeFtSe62Ip8T5S3A/l1hYHxLzwk4uwBYHJm7e2jeAYDhh7Z/at4LVtWFktxtoOx1814XYAZD15ieneRz25xb3gK7SlXdMclfJjlooPRVSX62u88df1czW3jmbrjnwLjMBdaRzAWWRf4ArJehXL9bVR04wrpyHdiWqjo6yYmZvMV7mvckuXN3b/XGhrHIW4DvUVWXS3LJgbJPbWNqZxcAiyNzdwHNOwBM1d2ndnct6k+Slwxs6QkzzPPmLf5r/s3A+J2q6mJbnHPIsZn+S++3MjnoBFi02w+Mf2IHD6fLW2DXqKpbJ/nbJBceKH19knt299nj72pLXjEwfnRVXWOeC1bVdZJcd6DslfNcE2BFyFxgWeQPwHo5IckZU8YvnTnfXl5Vl8jkDHWaoXNdYA/a+Fz4uiRD3+t8KMmx3X3a+LuambwF+H5Dzwmcnu295NPZBbBSuvuIBT4L+4SB7bxkhnlevIV/PZm7C2jeAYDkzUk+P2X8B5I8aM5r/srA+N+v0JuHgD1i4y1i9xso+5cdLPHmyFtgF6iqmyb5x0xyaZp/SXKP7j5r/F1tTXd/Msm7BsoeNudlhzL37d396TmvCbB0MhdYFvkDsF66+5uZnEdMM+9c/8VMf3HJ57KzM2FgDVXV1ZO8IcM3NHw0ye26++vj72p28hZgv44bGH9bd/dWJ3V2AbA4Mnd30LwDwJ7X3edl+MafR1TVwfNYr6pumOR2A2UvnsdaAFv0gCRXHqh57XYnl7fAblBVN0jy6iSHDJS+K8mdu3vaGxqX7c8Gxh9YVUfOY6GqunyGG0BfPI+1AFaUzAWWRf4ArJehXL9TVV1vHgtV1aEZfmjnJdt5SBNYX1V1pSRvTPJDA6UnJbltd3957D1tk7wF2FBVt0pyi4GybT8nEGcXAIskc1ec5h0AmHhWkmlvTL9ckt/c6SJVVUmOHyj7YHe/YadrAWzFxi9UTxso+0omD7TvhLwFVlZVXTuTLx8uNlD6viR32HhD4yr78yRfmjJ+cJKnzmmt38v0m4q+uLEfgHUlc4FlkT8Aa6S7X5/k36aUzHLuOatHJ7nMlPGzkvzxnNYC1kBVXTaTxp3LD5SenOQ23f2F0Te1TfIWYKKqDkvyJwNl5yR5+Q6WcXYBsDgyd8Vp3gGAJN3930leOlD26Kq68Q6X+vUkNxuo+b0drgHsclV1bFVN++Vm3utdKpOmnEsMlD6vu8/eyVryFlhVVXW1JG9IcsmB0n9Lcvvu/sb4u9qZ7j4zyR8NlN2/qn5yJ+tU1b2S/OxA2fHdPa15E2BXk7nAssgfgLU0dG55y6r6PztZoKpukuQ3BspevHGeC5Cq+sFMzk+vMlD6uSS37u7Pjr+rHZO3wEqpqmOqaugFc/Nc7+Akf5fkqgOlf9nd0x4En8rZBcDiyNzVp3kHAC7wuCSnTxm/UJK/q6qhA8n9qqp7JHnKQNm7k/zVduYH1sqjk3yyqh5aVRcZc6GNJpl3JLnuQOnXkzx9TsvKW2ClVNUVM/niedrbD5PkY0lu191fG39Xc3N8ks8M1Lxku02TVfXjGb56+zMZPiAEWGnBDwsAABmnSURBVAcyF1gW+QOwXl6e5D0DNb9XVXfdzuQbLzB5ZZKDppSdnuTx25kfWD9VdUSS1yX5kYHS/86kcefT4+9qLuQtsGqOS/Lpqnrsxo04o6mqqyd5U5LbDJSenfnklLMLgMWRuStM8w4AbNi4tvuJA2WXTfIvGx9AZlZVD87kIfFpB3PnJXlYd/dW5gbW1mWTPCuTJp7frarrz3PyqrpUVT0nyTuTXG2GH/mV7j51HmvLW2CVVNVlMmncueJA6X8mue1O3iy2DN19RpJfHSg7LMnrquouW5m7qu6e5LVJDh0ofWR3f3srcwPsRjIXWBb5A7BeNs4tH5pk2vnlhZL8TVX9/FbmrqqbJXlLkiMHSp/gFgggSarq0CSvTnK9gdKvZHJ+etL4u5oPeQusqIsneVKSk6vqj6rqZlVV85q8qg6tqicl+XCSWR7afmJ3f3Kn6zq7AFgcmbvayvNqAKySqnpxkgdMKXlCdz9+xPUPTPLPSW4xUHpOkucl+f1pV35X1Y9l8oD67WdY/ind/dhZ9wqsr6p6c5Jb7mfopCSvyCSn3t3dp21x3otn8uac+ya5UyZfOMziz7v7/ltZa4a9yFtgJVTVh5NcZ6Cskzw5w2+nmadPdPe/zGuyqvqLDF9b3Zm8bfJJ3f3xKXNdK8nvJLnPDEv/RXffd+aNAntKVd05ww+xfK+bJnnglPFPJHnaNrZzwkaT+Y7JXGBZ5A+wl1XVLZJcfYs/do0kvzZl/KtJHrWN7bxlHg+vV9VTkjxmhtLXJPmd7t709oiqOirJbyZ5UKa/+CiZPGx+m+4+d9a9Auurqv4pySwP9D07yQdH3s6+vtDdJ8xjInkLrIopz0x9PpPnBF6f5F3d/dUtzntYkp9I8r+S/GSSg2f80Tclud08c8rZBbCXVNXjkzxuSslLuvu4EdeXuStI8w4AK2XZzTsbe7hckvcnufQM5ecleVeSf03y2STfTnJ4Jl8Q3Tyzf1HkYA74f6Y07+zrvCSnJPmPTB4m/+8kX09y1sbYYZnk0eFJrpLkRzN8q8T+vDHJnbr7O9v42ankLbAKqmpVD0bmelC38YbM9yS55ow/8oEk70jy6STfzOTvlSsnuVkmf6fM4uNJbtTd39zaboG9YsbPvYtyq+5+8zwmkrnAssgfYC+b4fulRXpgd794p5NsvADpjZn9M/PHk7w1k5dAnZbkkCRXSPJjSX48ySxvbP9Skut3939tecPAWqqqk5Mctex97MdbuvuYeUwkb4FVsYXPtJ/L5DmBkzN5TuCrmTwncE4mv9uf/6zAFTP5/f6qmS2b9vXhJDfv7m9s8eemcnYB7CUr0Lwjc1fQUIc/AOw53f35qrpDJjdCHDFQfkAmb/296Q6W/ECSe3iQHNiiAzL5BenKI67xt0n+1xiNO4m8BVik7v5mVR2byZfKszRzXn/jz3Z9JsmxDuWAvUjmAssifwDWS3efW1X3yOSN59eb4UeumdkfyNmfUzPJdQ+SA3uKvAV2octv/BnL25Pcdd6NO4mzC4BFkrmr6YBlbwAAVlF3fyDJsZm88WZM70ly++4+deR1ALbi7CSPTXKv7j5zzIXkLcDidPdnktwmySdHXuo/k9x6Yz2APUnmAssifwDWy8Z55u2TvHfkpb6UyQM2Hxx5HYCVJG8BkiSd5JlJbtPdXx9tEWcXAAsjc1eP5h0A2ER3vzvJjZK8b6QlXpLkFt39lZHmB9iOtyT5se5+Sneft4gF5S3A4nT3f2aSua8daYnXJLlxd499+Aew8mQusCzyB2C9dPeXk9w8yUtHWuI9SW64cU4LsGfJW2CP+1AmTTsP7+6zxl7M2QXA4sjc1aJ5BwCm2OgE/vEkv5rk9DlN+6kkd+7u48a+0QLYtR6X5NmZ5MWivCHJnbr7mI3bcBZK3gIsTnd/vbvvkOS4zO/msy8leUB333HMt7EB7DYyF1gW+QOwXrr7zO5+QJK7ZH7nxqdnch570+7+7JzmBNjV5C2wZH+U5A+SfGSBa747yc8mObq737TAdZ1dACyQzF0dmncAYEB3n9Pdz0hypSSPTrLdq/3eneR+Sa7Z3a+e0/aANdTdb+nuh3b3VZNcM8nDk/xl5tvMc06Sd2WSa1fr7tt194lznH/L5C3AYnX3S5JcJclDknxsm9N8dOPnr9zdY72REmDXk7nAssgfgPXS3SdkcmZ8v0xucNiOUzI5f71Sdz+ju8+Z1/4A1oW8BZahuz/Q3b/e3ddJclSSByd5SZKPJ+k5LXNeJrfsPCnJj3b3j3X3y7v7vDnNv2XOLgAWR+YuX3XP6+90ANi5qrpHkutNKXlzd795QdvZVFX9aJJjM9nrjyQ5MslhSS6S5Iwkp2VyGPfRTB4iP7G7P7ec3QLrpKouleSGSa6WyS9TV87k4O5iSQ5NckiSiyY5O8lZSb6Z5MtJvphJ88/Hk/xbkn/t7m8tev9bJW8BFqeqrp7kDkmOTnLtJJfLJHMPziRzT0/yuUwy9/2ZZO5Jy9ktwO4mc4FlkT8A66WqrpDkjklulORamZwVH55Jrp+VSa5/IZMHcj6Y5LXd/aHl7BZg95K3wLJV1cWS3CDJNTJ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      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(samples, 4, target_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autocorrelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def autocorrelation(series, max_lag=1000):\n",
    "    x = series - np.mean(series)\n",
    "    xnorm = np.sum(x**2)\n",
    "    autocor = np.correlate(x, x, \"same\")/xnorm \n",
    "    origin = int(np.floor(len(autocor)/2))\n",
    "    return autocor[origin:origin+max_lag]\n",
    "\n",
    "def plot_autocorrelation(sample, color, label):\n",
    "    plt.figure(dpi = 600)\n",
    "    plt.hlines(0, 0, 1000, colors = \"k\", linestyles = \"dashed\", alpha=0.4)\n",
    "    plt.plot(autocorrelation(sample), color=color, label=label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t/jxNnTo1eea9AmJHDjjggKIKv1OmTEmeAfgwBQQAatLuu+8eAwcOTJpRQICPuuuuu2LOnDlFz19++eW+nQUAoAImTpxY8jvHjx9f8jsBgK7ZuHFj3HzzzWV556tf/WrMmzcv97eA8rvrrruSZwYOHBhPPfVUDBgwoEtv77333nHxxRcnzzU0NMTq1au79DZUm2IKCO99KVuvXr1ixIgRRb07ceLEqKuri6uvvjoWL178gc9Wr14d48aNixEjRsSBBx4Yu+++e+y7775x8803x8aNG4t6r5ImT54cS5YsqXSMTvvFL34R559/vn/W9j6/+MUv4oorrijZff68C8pjw4YNuRZ+2tvb47/+678+8usvv/xyXHLJJXHkkUfG4YcfHscee2yMGjUqVqxYkVuWVHkWECIivve97yXfr4AAlIICAgA16+CDD046P3PmzFi7dm1OaaA23XTTTV2af+aZZ+Lpp58uURoAADrrhRdeKPmd/oEsAFSfX//612X7IbrVq1fH+eefX5a3gPJpbm6Oxx9/PHlu1KhRJcvwne98J/bdd9+kmba2tlyK11BJr7zySvLMexsQIiLOOuusUsbZojlz/h97dx5QY9r+Afz7tIiSsoxlLNnKWpZIyVKJSIzdMBiMdZgxlnkZu2EYvIx9whgGJYwsTYmiLMmSUCnJFo2kIrQv5/z+8PKzVee+z/Ocrevzzzvque77mnl1es5z7uu67mHSpElwdXXVus9Wjx49qu4UmG3atAk///yzutNQq2fPnmHWrFkQBAFTp04Vde1Lly6JOkWUEPJpZ86cQXZ2tqR7/PTTT2+nxpw5cwY9e/ZE69at8csvv+DixYu4evUqgoKCMGXKFFhbW2vMlD/WAgQjIyM0a9ZM4eutra1RqVIlSXMihJBPoQIEQgghWou1AEEul3N1FiFEV12/fh3Xr19Xeh2agkAIIYQQolqZmZlchxZKc/r0aRQWFoq+LiGEEEL4bdq0SaX7HT16FH5+firdkxAirUOHDjHf59epUweurq6i5VC+fHnMnTuXOe7kyZOi5UCIJmD9nLJmzZqoVq3a2z9369YNLVu2FDutYp07dw5DhgxR2X5iOHLkiLpT4LJ48WJ4enqKumZubi727NmDvn37wsbGBu3bt8fo0aPh5eWF9PR0UfdSRlhYGFq3bo01a9ZIsn5RURFCQ0MlWZsQ8vpZdWRkpOjFQ8Vp1qwZ+vXrBycnJ5w4caLY6x4/foxu3brB19dXJXkVRyaT4dq1a0wxNjY2MDQ0VPh6PT09pokJwOt7kry8PKYYQgj5EBUgEEII0VqsBQjA6/FrhJDX/vrrL1HWOX/+PE6dOiXKWoQQ6RUWFiIwMBDz58/HnDlzsHbtWjx8+FDdaRFCCGFw5cqVt52exPTy5UtERESIvi4hhBBC+ERGRuLChQsq3/e7777Tum7HhJDieXt7M8eMGjUK+vr6oubh5ubGHEMFCESXFBUV4ebNm0wxNjY27/1ZT08PW7duFTOtUgUEBODcuXMq3ZPX7du3ERcXxxRTuXJlBAYGwsLC4pPfr1atGnbt2sX1uTSradOmidJwQiaTYc+ePbC0tMSoUaPg5+eH6OhoRERE4K+//sKIESNQvXp1fPHFF8zPgbKzs1FQUKB0jm+EhobC1dUVjx49Em3NTwkODpZ0fULKomfPnmHGjBn4/PPPYWtri4SEBOY1qlatyhxz+/ZthafdFBUVYejQoTh27BjzPmK5d+8e8xQW1mICALC1tWW6vqCgADExMcz7EELIu6gAgRBCiNaiAgRC+BUUFMDLy0u09WgKAiHaITg4GBYWFujVqxd++eUXrFy5EjNnzoSFhQWGDRuGzMxMdadICCFEAVIeRAwKCpJsbUIIIUQmk9HzAwYbN25Uy76JiYn45Zdf1LI3IURcjx49wtmzZ5njvv76a9FzqV27Nlq0aMEUc+/ePdy5c0f0XAhRhzt37iA3N5cpxtra+qOvdezYEXPmzBErLYWsW7dOpfvxUvRA6rs8PDzg5uaG+Ph4/P777/j666/RqVMnDB06FP/9739x69YtfP3111i6dKkEGb8vPz8fU6dOVep++e7du7C3t8eoUaOQlJRU7HUymQzHjh1D+/btsXLlyhLXjImJQd++fVGzZk2YmJigSpUq8PDwwKFDh7jzBICLFy/Cw8OD+eeCBxUgECKupKQkdO7cGb/99htevXrFtYaFhQVWrVolcmYfKywsxKBBg9T2OhAZGckcw1OAwBNz9epV5hhCCHkXFSAQQgjRWvXr10elSpWYYqgAgZDXjh8/jtTUVNHWu3DhAj28I0TD7d27Fz179sTjx48/+X0fHx84ODggJSVFxZkRQghhFR4eLtnaVIBACCFEbE+fPsXMmTPRokULGBsbo3r16ujRowd8fX2pGKEEOTk5OHDggNr2X7NmDe7du6e2/Qkh4ti/fz/za62trS2srKwkyYemIJCyLDo6mjnmwwkIbyxfvhxjxoxRNiWFnTx5Enl5eSrbjxdPAcIXX3wBADAyMsKkSZOwa9cunDt3Dj4+Ppg5c+bb7tweHh6ws7MTNd9POXfuHPfrXlhYGNq3b48rV64wxc2ZMwebN2/+5PeWLFmCVq1awc/P7+1nB5mZmfD398egQYMwYcIE5OTkMOcaHR2Nnj17qmzqVlxcXIkFGYQQxcnlcgwePBixsbFKrePu7o7BgwfD2NhYpMyKV1BQgKFDhyImJga5ubmQy+V49uzZ2+kEUj6bUFUBAusEBIAKEAghyqMCBEIIIVpLEATmKQg3btyATCaTKCNCtMeuXbtEX3PDhg2ir0kIEceTJ0/w7bffoqioqMTrYmJi4OHhobKH/oQQQtjJ5XJcvHhRsvXDw8O5u1YRQggh75LL5fj999/RoEEDrF27FrGxscjLy0NaWhqCgoIwcOBA9OzZE9nZ2epOVSNduHBBrf9t8vPzMXv2bLXtTwgRx759+5hjhg8fLkEmr/Xo0YM5hgoQiK7gKUD41AQE4PVnpNu2bXt7eF5qmZmZOH/+vEr24pWamso8MdLIyEjhwihBELBs2TKe1JjNnTuX+TBsZmYmRowYgefPn3PtOXXq1I+eN3l7e2Px4sUlfra+fft2DBw4EAUFBQrv9eLFC/Tr1w8vXrzgypUXT4EKIeRjBw4cEOX5tLu7O0xNTTFo0CARsirds2fPYG1tjQoVKqBcuXKoWrUqGjVqBDMzM1SqVAnff/89nj59Kvq+rL8/DQwM0LJlS+Z9LC0tYWpqyhTDUxxBCCHvogIEQgghWo21ACErK0vSbqGEaIPU1FT4+fmJvm5gYCD3g01CiLR+/vlnhQ+TRkREYOjQoSgsLJQ4K0IIITwSEhKQnp4u2fqFhYU4e/asZOsTQggpGzIyMjB48GB8++23JR6iP3nyJIYPH15qsXRZFBERoe4U8Pfff9N9ASFa7O7du8yHigRBwNChQyXKCOjcuTOMjIyYYk6dOoX8/HyJMiJEdaKiopiu19fXR7NmzYr9voGBAby8vIotUhCbv7+/SvbhFRISwnxo39XVFRUrVmS6vkuXLqypMYuMjISvry9TzKpVq/DgwQOl9nVwcICLiwtOnDiBpKQkjBs3TqG448ePY9y4cUhLS0NGRkaJ/z/I5XKMGzdOLZO2/v77b5XvSYgu2rRpk9JrGBkZwdnZGQBUOtHnjQ8/A83MzMTGjRvRqFEjUd8Dv3z5krlYo0WLFihfvjzzXnp6emjTpg1TTFRUFN1nE0KUQgUIhBBCtBprAQIAbN26VYJMCNEevr6+khwsLiwsxOHDh0VflxCinOzsbOzdu5cpxt/fH//5z38kyogQQogyVFFQHRQUJPkehBBCdNeTJ0/g4OCAQ4cOKXT90aNHNf5AmzpoQgECAPzwww9UIEKIlgoJCWGOcXJyQu3atSXI5jVjY2N07tyZKUYbOq8TogjWCQhWVlalHkA0MTGBr68vzMzMlElNIQEBAZLvoYzQ0FDmmH79+jFdLwgCVq9eDQMDA+a9WM2fP1/hz/JycnKwZcsWUfYNCQlBz549UbduXeTk5Cgct3v3bnz22WeoXLkyTExM0LVrV6xcuRJxcXHvXbdlyxa1FQKcPXsWKSkpatmbEF1x9+5dUe7LnJ2dYWJiAgDo2rUrOnbsqPSaYsjMzMTAgQORmJgoynpnzpxhfj/dtm1b7v1sbW2Zrs/Pz8fNmze59yOEECpAIIQQotV4ChC8vb1x584dCbIhRDtI+ZD4wIEDkq1NCOFz5MgRhacfvOu3337DuXPnJMiIEEKIMqgAgRBCiCYrKChAjx49cOvWLaY4T09PiTLSXlevXmW63szMjKtLYmmuXbuGffv2ib4uIUR6YWFhzDHDhw+XIJP3ubm5Mcdo+sFnQkrz8uVL5o7vik42aNy4Mby8vCAIAk9qCouPj8fdu3cl3UMZrAUIgiCgT58+zPvY2dlh9erVCl1brlw59OnTB6ampsz73Lp1Czt37lTo2n379kk6LZNVTk4Ozp49izlz5qB58+ZwcnLC+fPnERYWhhkzZoiyx5o1a9C1a1emGJlMhiNHjoiyPyFlFWvDs+IMHDjw7T8LgoA///wTlStXFmVtZaWlpWHMmDHMU3U+hec5O+tr27t4ihdYnz0QQsi7qACBEEKIVmvZsiX09fWZYoqKivDzzz9LlBEhmi03NxfBwcGSrR8cHIy0tDTJ1ieEsNu1axd37MaNG8VLhBBCiCguXLjAdL2RkREaNGjAFBMbG4t///2XKYYQQggBgD///JO5uy8AnDp1Crm5uRJkpJ2ePXuG+/fvM8U4OztzHaJTxMqVK0U5fEEIUS3WAgRDQ8P3DoNJpUePHswxNCmHaLtLly4x/y5VtAABAHr37g0fHx+UK1eONTUmx48fl3R9XikpKR912i9NmzZtUKNGDa79pk2bhvXr1xdbWGBubo6pU6ciNjYWx44d454ePmHCBGRmZpZ4jVwux/r167nWV5UzZ86gc+fO6NSpE/Lz85Ve75tvvsH06dO5fmepa/oCIbpALpdj9+7dSq9Tv359jBo16r2vNWnSBCdPnkTVqlWVXl8MISEhojTp4TmX4erqyr0f6wQEALhx4wb3foQQQgUIhBBCtFqFChXg4uLCHOfl5YX4+HgJMiJEs509exbZ2dmSrV9UVMT9IJUQIr6kpCSlio78/f0VHvNMCCFEemlpaYiJiWGKsbW1Re/evZn3krJolRBCiG6Sy+VYu3YtV2x+fj4iIyNFzkh78XQgtLW1xYgRIyTIBoiJiUFgYKAkaxNCpJGSkoKEhASmGEdHR5V0nrW2tkbt2rWZYm7dusXcPZ4QTcIzzZClAAEAhgwZgtOnT8PS0pJ5L0VpajHQmTNnmGOcnZ259xMEAd9//z3i4+OxevVqjBo1Cm5ubpg1axYOHDiAx48fY+PGjWjUqBEAoFu3bvjiiy+49jI1NcWECROwa9euT045OHPmDKKiorj/XbTNTz/9hK1bt0IQBAwYMIA5PiQkhBqpEcKosLAQMTExWLRokdL3Y3p6evjvf//7yYK5du3a4fz586hTp45Se4jlp59+gkwm++jrycnJWLNmDYYNG4YvvvgC3333HXbv3o0nT568d11SUhJzcVyzZs2Y75PfZWVlBRMTE6YY1gmWhBDyLipAIIQQovV4PliTyWSiVGcTom1U8XB4//79ku9BCFHM3r17lepSmZ2djdjYWBEzIoQQoowTJ04wv647ODhwdU2iAgRCCCGsrl69itu3b3PHs0750WW8BQh9+vTh6nioiNWrV0uyLiFEGjyvqY6OjhJk8jFBEODu7s4cFxAQIEE2hKgGz8+knZ0dc4yjoyOioqKwcOHCYqch6OvrY/To0VzFnyEhIZI2ueIVGhrKHOPk5KT0vrVq1cKsWbPw119/ITAwEKtXr8bgwYNRoUKFj65dunQpBEHg2mf79u0YM2YMatWqhZ9//hkFBQVvv6fp0w/E0r9/f/j5+WH58uXQ19cHANSuXRsdO3ZkWqeoqAhHjx6VIkVCdEp6ejrWr18PNzc3VK5cGdbW1li6dKlSa5qamsLPz6/E6SVNmzZFWFgYmjdvrtReYoiMjHxvasqbpgtWVlaYNWsWfHx8cOzYMWzatAlff/01atWqBXt7exw6dAgymQynTp1i3rN79+5K5ayvr49WrVoxxVABAiFEGVSAQAghROsNGzYMDRs2ZI7z9fWVIBtCNJsqPqQJCQlBSkqK5PsQQkoml8vx119/Kb3OlStXRMiGEEKIGHju5Tp27AgnJ6e3H84qKjg4WKkiNkIIIWWPl5eXUvFhYWEiZaL9eAsQBEGAp6cnzMzMRM8pJCQEERERoq+rDvn5+dizZw+GDBmC7t27o1evXli3bh0SExPVnRohouF5TVVVAQIAriltmtp5nZDSyGQyXLx4kSmmfv36qFWrFtd+5cuXx5IlS/Dw4UMsX74czs7OsLW1hbOzM1atWoU7d+5g586daNOmDXr06MG0dl5eHs6dO8eVl5RYCxD09PTQqVMnaZIphrW1NYYNG6bUGgUFBVi0aBFGjhwJmUyGqKgonT9M37dvX+Tk5MDX1xceHh4ffX/QoEHMa4rxuQkhukoul8PT0xN16tTBDz/8gJMnTyIzM1Ppdfv374/IyEiFilDr1auH8PDwT/7Mq9r8+fPfFn2tWrUKM2fOLPG/x6VLlzBo0CC4urriyJEjzPvxNBL6UNOmTZmuT0pKEuX/Y0JI2UQFCIQsSlDVAAAgAElEQVQQQrSeoaEhFi1axBwXHx+vkV06CJHK7du3cefOHaaY+vXro127dkwxMpkMx48fZ4ohhIjv8uXLonStoAIEQgjRDEVFRThx4gRzXKdOnWBmZoYOHTowxT158gQxMTGf/J5cLseNGzewdOlSzJw5E8uXL8epU6eoYIEQQsqwoqIi+Pj4KLXGhQsX6HfJ/7Ae9K9bty6qV68OAGjXrh1CQkKKbdiip6eH+vXrc+WlC1MQrly5ggYNGmDUqFE4ePAggoODERgYiOnTp6NRo0b45ptvcP/+fXWnSYjSWAsQBEGAg4ODRNl8rFu3bsV2Zy+OpnZeJ6Q0cXFxePHiBVMMa1f3T6lRowZ++uknnD59GhERETh9+jR+/PHH9+4DeIqBwsPDlc5NTCkpKYiLi2OKadOmDczNzSXKqHhLliyBgYGB0uvs378fq1atwjfffKPT988eHh44fPgwypcvX+w1JXVSL865c+cQHR2tTGqE6KwVK1Zg8uTJyM3NFWW9rl274ty5c/D19UXjxo0VjqtUqRKOHDmCOXPmcE+PEUNCQgL8/f0RHByMOXPmKBwXEhLCXICgr68vynQe1gIEAEpNsySElG1UgEAIIUQnfPXVV0xvWIDXh2bi4+MlyogQzcPTMdfd3R1Dhw5ljtO0B9CElEV//PGHKOvoSodLQgjRdleuXEF6ejpTTPv27d8eRuQZ3+zn5/fR15KSkuDg4IDWrVtj4cKFWLt2LebNmwdXV1c4ODhQ4RohhJRRp0+fxpMnT5Ra4+nTp7h3755IGWmv9PR0PHjwgCnG1tb2vT+3adMG8fHx8PHxwYQJE9CrVy+MHDkSmzZtQmJiIu7fv4+RI0cy5/b333/j6dOnzHGa4tKlS+jWrRseP378ye8XFRXhzz//hJWVFebOnYu8vDwVZ0iIOHJycpgnqbRo0UKlh3ErVqyIrl27MsXk5eXh9OnTEmVEiHQuXLjAHKOqgiBFOlF/iHWag9TOnDnDHCPGAU8ejRs3xuTJk0VZ66efftLpZ/d169bFrl27oKdX8rG2evXqMTfdAIDNmzfzpkaIzrp48SIWLFgg2nq9e/dGaGgo98QZfX19rFixAhcuXBClMI/X1q1bMW7cOMn3sbe3h6mpqdLr8BQgiNHQjhBSNlEBAiGEEJ2gr6+PESNGMMexdsQgRJvxjKh2d3fH4MGDmeMuX77MHEMIEc+zZ89EK0CIioqiQxeEEKIBeItJ3+ApQDh06BAA4PHjx9i/fz9GjBiBunXr4tKlS5+8/tKlS+jRowd1kSNlXkJCAhYsWIAxY8Zg2LBh2LBhA/M0OkK0jZeXlyjr8BzQ0zWRkZHMMZ+aXmlgYIChQ4di69atCAgIwO7duzFlyhTUqVMHADBr1izmfWQyGdfzJU0gl8sxefJkvHr1qtRrCwsLsWLFCgwcOBD5+fkqyI4QcV2+fBkFBQVMMY6OjhJlUzyezushISESZEKItHjub1R10LJx48Zo0KABU8zFixchk8kkyogdz72JugoQAGDhwoWoVKmS2vbXBvr6+vDx8UHVqlUVun7QoEHMe+zdu5d5Mgkhukwul2P69Omivr7/+OOPoqxjb2+PsLAwXLhwAYMHD4aJicl732/ZsiX27NmDbdu2ibLfhwIDA5GYmCjJ2u/ieX7/KVSAQAhRJSpAIIQQojOGDBnCHEMFCKSs+Pfff5m7Q5UvXx7Ozs6wsLCAnZ0dU2x0dDSNwyZEDV68eIF58+Yp/GBeEQUFBbhx44Zo6xFCCOFz/Phx5phevXq9/Wc7OzvmDkqRkZGwtbVF7dq18eWXXyp0uDQjIwM9e/bE8+fPmfMlRNvl5uZi5MiRsLKywrJly7Br1y74+Phg2rRpsLS0hIeHB0JDQyGXy9WdKiGiysnJga+vryhrUQEC3xS6DycgKMLGxgZubm7McdraaTcoKAjXrl1jivH398d3331Hr9tE63zzzTfMMeooQODpvB4TEyNBJoRIi3VitLGxMWxsbCTK5mOsxQ4vXrzQmAnzSUlJ2LdvH1OMnp4edzduMVSrVg1z585Vy956enqoWbOmWvZmsWHDBqa/l4MHD4YgCEx7ZGVlYffu3aypEaKzDh8+LOqEGwsLC3Tu3Fm09YDX04EOHDiA58+fIzw8HOfPn8ft27cRFRWFESNGYOzYscwTtjSJq6urKOs0aNAAhoaGTDFUgEAI4UUFCIQQQnSGpaUl8400FSCQsuLPP/9k7ljg7OwMY2NjAGB+QFBUVMTVsY8QwqeoqAhbtmxB48aNsXz5ctHXv3LliuhrEkIIUVxKSgrzYb9q1aq91w3Z0NAQLi4uzHvz3NM9fvxYbR+mE6IueXl5cHNzw969e4u9xt/fH87OznBzc8P9+/dVmB0h0goMDFSoq7wiqAABuHr1KnMMTwECwNeRMioqimsvdTt48CBX3LZt2/D777+LnA0h0vH29sbdu3eZ49RRgGBpaQlLS0umGE059EyIotLT05n/3trZ2cHAwECijD5mb2/PHMNaVCGVNWvWME98adOmDczNzSXKSDHff/896tatq/J99+/fj1u3bmHevHkaO4Xhxx9/xLfffssUY2FhwVXUtmnTJhQWFjLHEaJrCgoKRJtW8Ma0adOgpyfNsVRDQ0PY29vD0dERlpaWbwuQ9PX14eXl9XbqnzYxMzNjbghZHAMDA+Z7bCpAIITwogIEQgghOoPnRpoKEEhZUFRUhD/++IM57t2HdTxveC9fvswcQwhhl5qaip49e2LKlClIS0uTZA9t7XBJCCG6YuvWrcwxPXv2hL6+/ntf69+/v1gplcrT0xNBQUEq248Qddu8eTPOnj2r0LVBQUFwdHTE7du3Jc6KENUICAgQba3o6Gi8fPlStPW0jVwuZy7CqFevHj777DOu/VxcXJg7LEdFRWndRAC5XI6TJ09yx0+bNg2hoaHiJUSIRKKiojB27FjmuJo1a6JBgwYSZFQ61i7kiYmJNHmXaBWebtKsEwmU5eDgwByjCQUIqamp8PT0ZI5zcnISPxlGFSpUwKZNm1S6p62tLQYMGAAzMzMsW7YML1680LgDp0OGDMGvv/7KFTtlyhTmmNu3b2Pbtm1c+xGiK+RyOcaMGYN79+6JtmajRo0wadIk0dZjUbt2bcTExGD8+PEfFUBUrFgRS5YsQXBwsFpyK8nQoUOZm62WpGnTpkzX3759G0VFRaLtTwgpO6gAgRBCiE5p1qwZ0/UJCQnU2YCoVEFBAR4+fMjckUUZQUFBePjwIXNc79693/4zFSAQopkKCwvRv39/yR+W0QQEQghRnydPnmDVqlXMcb169froa3379hX1g4zS9OjRA1u3btW6Q4qEsMrKysLSpUuZYpKTk9GnTx+8ePFCoqwIUQ25XI7jx4+Luh7PQT1dce/ePSQnJzPF8E4/AABBEJinXr58+ZLrOZM6JSQkKJVzYWEhRo0aJdqkD0KkUFRUhDFjxiAvL4851tHR8W3nWFVr0qQJcwwVcRJtwlPApuoCBBsbG1SoUIEpRt33a28OzObm5jLH9unTR4KM2PXt2xeLFi1S2X4rVqz46DBukyZNkJqaiiFDhqgsj+IMGzYMf/31F3fHdDc3NzRs2JA5bsGCBXj27BnXnoRoO7lcjh9//BFeXl6irrt9+3bm3ytiMjMzw7Zt25CRkYFTp05hz549uHDhApKSkrBw4UJ06dIFFStWVFt+n8I6+aU0rPfYeXl5Wvc+nxCiGagAgRBCiE5hLUAoKCjgGkdMCKuQkBB07doVlSpVgoWFBcqXL49WrVrBx8cHMplM0r15unc4ODi81/XKwsKCuZseFSAQIr1Vq1YhLCxM8n3i4uKQmZkp+T6EAEBOTg5evXol+e9HQrTFokWLkJWVxRQjCALc3Nw++nrlypXh6uoqVmoKmTRpEgYOHIicnByV7kuIKh09ehQZGRnMcbdv38bIkSPpdx7RatHR0fj3339FXTMkJETU9bTJ+fPnmWPat2+v1J7W1tbMMdHR0UrtqWpiTGV69OgRV5dlQlRl165diIyM5IplLUQSE2t3VgAa17GbkJL4+fkxx9jb20uQSfEMDQ3Rrl07ppibN2+qtZj6119/hb+/P3NckyZN1Pqa96FFixZh9+7dqFmzpqT7uLi4FPs8qFq1ati/fz8CAwMxbdo02Nvbw9XVFRMnToSnpyfOnz+PkydPIiQkBA8ePIBcLkdgYCBMTU0/uZ4gCLCxsUGjRo0Uys3ExARr166Fl5cXypcvz/3vqKenh8mTJzPHPXv2DIsXL+belxBttmrVKqxZs0bUNSdMmABnZ2dR1+RlamoKFxcXjBgxAg4ODjAzMwPw+vce6xQuKTk6OqJVq1airkn32IQQVaECBEIIITqlefPmzDFxcXESZELIa3l5eRg3bhxcXFxw9uzZt91YZDIZoqKiMGzYMPTt2xf5+fmS7J+YmIhjx44xx02YMOG9PwuCgA4dOjCtcf/+faSmpjLvTQhRTEpKClasWKGSvWQyGa5du6aSvUjZFRISghEjRqBq1aqoVKkS9PX10bp1a+zbt48mVpEy6+bNm/jjjz+Y4+zt7VG1atVPfm/QoEHKpsXs8OHDmDhxIk1CIDprz5493LF+fn5YtmyZiNkQolpiTj944+TJk6KvqS3OnTvHHKPswQkbGxvmmKioKKX2lFJBQQGuX7+O06dPIyQkBDdv3sT+/ftFWfvQoUOirEOI2F6+fIm5c+dyxQqCgAEDBoickeLocBTRZbdv30Z8fDxTTJMmTYp9Py8lBwcHpuvlcrnapuZ6e3tzv+bNnj2bu8O+FARBwMiRI3H79m1s3boVjo6OkuyzYsWKUifduLm5Yd26dQgPD0dQUBA8PT0xceJEODo6onv37nBycoKFhcXbax8+fIhffvkFHTt2ROPGjdGsWTNMnToVYWFhuHHjBuLj4+Ht7V3sZ/d6enoYP3487ty5g+nTp4syiWfs2LFcRQxbtmyh3y2kzLl48SLmzZsn6pq1a9fmmuSrDk5OTupO4a0pU6aIvibdYxNCVEVz7qwJIYQQEbBOQACoAIFIJyMjA7169cKOHTtKvM7f3x9jx46VJIfZs2ejqKiIKcbMzOyT41bt7OyY91fXA2hCyoLFixerdCqBusdqE9318OFD9O/fHy4uLvDy8nqvS/qNGzcwfPhw2Nra0sNPUiYtXryYqzN6SUUGX3zxBfT19ZVJi8uePXvg6+ur8n0JkVpKSorSh6UXL16s0Yd5CSlJQECA6Gteu3atzDY0YJ2AUK5cOaUnILRs2ZI5RhNfs/Ly8rBgwQLUrVsXbdq0Qbdu3eDi4oKWLVtyFXZ8ytWrV982FyFEk/zyyy94+vQpV+yYMWNQt25dkTNSXMOGDWFgYMAUw3qgmxB14Zl+oK6OzDxTF8LDwyXIpGQPHz7EpEmTuGLr1KmDr776SuSMxGFqaooJEybg/PnzePDgAQwNDUVb+6uvvuL6fK805ubmmDt3LsLCwpCQkIDY2Fhs3LjxbTGLvr4+hg0bhujoaISGhmL69Ono168fhg4ditWrV+PRo0fYtm2bqNMfqlSpguHDhzPHFRUVac2haULEIJfLMX36dOYzBCUpX748fHx83k4Z0HSaUoBQo0YNDBw4UPR1mzRpwhxDn8ERQnhQAQIhhBCd0qRJE+YOCVSAQKSQkpKCzp07IyQkRKHrvby8RP/w9uzZs1wd3r766isYGxt/9HWeB5SXL19mjiGElC4uLg7bt29X6Z7BwcEq3Y+UDdu3b0ezZs1w5MiREq+LiopCu3btlOowTYi2SUxM5Dqwb2pqitGjRxf7/apVq8LFxUWJzPgNGjQIz549U8vehEhl3759XIVC75LL5Zg9e7ZIGRGiOi9evEBYWJjo68rlcpw6dUr0dTXd06dPmQ/Vtm/fnqvL67tMTU3RsGFDphhNK0BIT0+Hi4sLli1bhpSUFMn2KSwsxI0bNyRbnxAeDx48wLp167hiLS0tsWbNGpEzYmNoaIjGjRszxdDhKKIteKZTe3h4SJBJ6VgnIADqeV7s6emJV69eccXOnDkT5cqVEzkj8VlYWODp06f4/PPPlV6rcePG2Lx5swhZ8dPT00PXrl2xdu1aHD58GD4+Ppg1a5Yo/36fMnv2bObCNgDYv38/Xr58KUFGhGie06dPi9p0rHLlyjh48KDaiuh4tG3bFhUrVlR3Ghg/frwkv5vMzMxQq1Ytphi6xyaE8KACBEIIITqlQoUKqF+/PlMMFSAQsRUUFGDw4MGIiYlhitu4caNoORQVFWHatGlcsRMmTPjk13k66lEBAiHS4JluoqyzZ89Sp0ciqp9//hkTJkxAdna2QtdnZWVh1KhRWLJkCeRyucTZEaJ+v//+O9eh5jlz5qBKlSolXlPShASpVa1aFStWrOA+MECIptm7d68o6wQGBuLevXuirEWIqgQHB0v2vkTZySLaiHX6AQB07txZlL2tra2Zrr99+7ZGvT+cMmUKLly4oJK9aNon0TQbN25Efn4+c5yZmRn8/Pxgbm4uQVZsWDu0xsfHK10ASojU0tPTmX+3GxkZoXv37hJlVLKaNWsyf7569uxZtGnTRqWTEP7++2+uuKpVq2L8+PEiZyMdc3NzHDhwAEZGRtxruLu74+LFi1rTjVwsVlZWmDp1KnNcdnY2Dhw4IEFGhGieZcuWKRXfokULVK1aFY6Ojpg7dy7u3LmjtgI6XoaGhmovmDAxMcHkyZMlW79p06ZM11MBAiGEBxUgEEII0TnNmjVjuv7WrVt0iI2IasmSJVyj3QMDA0X7u7hz505cv36dOc7Ozg6tWrX65PeqVKnC3I3q8uXL9PNFiMguXbrENb5bWbm5uZJ0NyVlU1BQEBYtWsQVu3jxYq4JP4Rok5ycHK5JN3Xq1MEPP/xQ6nX9+/eHoaEhT2qimDt3Lrp06YKnT5+qLQdCxBAXF4erV6+Ktp63t7doaxGiCgEBAZKtffLkyTL3PIGnAEGsAxM2NjZM1xcVFWlMU5fg4GCVvj+gAgSiSWQyGdf9g56eHg4cOMB88F8qrIejcnJy8OjRI4myIUQcAQEBzIUyrq6uMDExkSij0tnb2zPHXL9+HR07doS7uzsSExMlyOr/paamIiEhgSt2xowZav1vy8PR0RFHjx5VuIO1np4ebG1tMWPGDFy7dg3+/v6oWrWqxFlqpoULF6JatWrMcX/++acE2RCiWc6fP4/Q0FCu2Hr16iE9PR0xMTFIS0vD+fPn8csvv5TaDEdTOTk5qXX/JUuWSDYNBmC/x3769CmeP38uUTaEEF1FBQiEEEJ0DmsBQmZmJpKSkiTKhpQ1SUlJ3GOrk5KScPfuXaVzKCgowNKlS7liJ06cWOL37ezsmNZLT0/H48ePuXIhhHwa78/3u/T09Jg7XAKvD40ToqycnBylu7rMmDFDozquEiI2b29vPHv2jDlu2bJlMDY2LvW6zz77DIMHD+ZJTTTXr1+Hq6srsrKy1JoHIcr47bffRF3Py8urzB24JtpLLpcjMDBQsvX//fffMtd9j7WjryAIcHR0FGVv1gIEAIiKihJlb2WtWrVKpftFRESodD9CSnL9+nU8efKEOW7SpEno0aOHBBnxYT0cBVCHVqL5eBrI9OnTR4JMFKfMfcXx48fRokULrFmzBnl5eSJm9f94J263atUKs2bNEjkb1XBzc0NcXBx+++03uLm5fVSMYGhoCCcnJ2zbtg2pqamIiIjAmjVr0Lp1azVlrBkqV67M1eE9PDycfr8QnSaTybBgwQKuWCMjIxw8eFBriw0+RYwCBCsrK8yYMYM5zsPDQ6FGQsrgKTaOj4+XIBNCiC6jAgRCCCE6h7UAAYDGdOwi2m/JkiVKHYgMCQlROgcvLy88fPiQOa5Bgwb46quvSryGtQABAG7evMkcQwj5tMjISPj7+yu1hru7O86cOYNDhw4xx1IBAhHDihUrlC64S05OhpeXl0gZEaJZ5HI5NmzYwBzXqlUrjBgxQuHr58yZw7yH2KKjozFu3Dg6cE20Unx8vOjdEW/duoVr166JuiYhUrl165bkDQcWL14s6fqaJDQ0lLmbt7W1NczNzUXZX1sLEG7evKny96lxcXF49eqVSvckpDg8z4jMzc2xZMkSCbLhx1OAQIejiCbLycnB8ePHmeM8PDwkyEZ1+2dlZWHWrFlo1qwZ9u/fL/p7/YsXL3LFHThwAOXKlRM1F1UyMzPDDz/8gMDAQDx+/BhZWVlISEhAXFwcXr58iZCQEIwfP16nDgWLYdy4cbC0tGSO27lzpwTZEKIZli5dyj39IDAwkOucgCaztbVV+j31unXrsGzZMjRq1EjhGBsbG3h7e0NfX1+pvUtDRb6EEFWgAgRCCCE6h6cAITo6WoJMSFkTFxen9AEUZQsQioqK8Ouvv3LFrlmzBkZGRiVe07ZtW+Z1Y2JiuPIhhHyMp2sPAAwfPhy3bt1CZmYm/P390alTJzRu3BgWFhZM60RGRiI1NZUrB0KA1w8veX9PfWjt2rV0aJnopPPnz3Md6Pvxxx+ZPrSwtrZG3759mfcRm4+PD9avX6/uNAhhtmDBAhQVFYm+LhXYEW0RHBws+R4HDhxAZGSk5PuoW2ZmJsaMGcMc16lTJ9FyaNSoESpUqMAUownPU9etW6fyPeVyeZn4e0m0Q0BAAHPMokWLUK1aNQmy4cfTnZUORxFNFhAQgMzMTKaYdu3aoXbt2hJlpJj69euLMh3l/v37+PLLLzF+/Hjk5+eLkNlrly5dYo45ceIErKysRMtBExgbG6Nx48Zo2rQpypcvr+50NJa+vj6++eYb5rjdu3ejsLBQgowIUa/AwEDuItTdu3eLMi1A0xgYGGDYsGHc8a1bt0bPnj1RoUIF7N27V6H31DVr1sQ///wDU1NT7n0VRQUIhBBVoAIEQgghOoenAIHnoRUhH5o3bx5kMplSa4SEhCh1mPLw4cNc3Z9cXFzQr1+/Uq9r0aIF89o0AYEQcURHR+Pw4cPMcdbW1ti9ezeaNGkCExOTt18XBAHdu3dnXu/UqVPMMYS88f3336OgoECUtWJjY3Hy5ElR1iJEk+zdu5c5pmbNmhg8eDBz3E8//cQc80bFihWRnp6O//znP9xrvDFr1ixcvnxZ6XUIUZWIiAgcPHhQkrX37dsnSWEDIWJT1fuCPn36SD5pQd1++OEHPHjwgDmuc+fOouWgr6/P/Mzn+vXrai0ITk1NxZ49e9Sy95UrV9SyLyHvSktLY/5cw9zcHN9++61EGfGrXLkyqlevzhRDh6OIJvPx8WGO6dOnjwSZsPvuu+9EW2vHjh3o37+/KPcLMpmM67NcMQoqiPYaOXIk9PTYjsU9efJEJcXWhKhSbGwshg0bxvV63L17d4wcOVKCrDTDwoULUblyZa7YuXPnQhAEAIC9vT0OHjwIAwODYq+3trZGWFgY6taty7Ufq7p16zI3GqB7bEIIKypAIIQQonMqV66MmjVrMsWEh4dLlA0pK27cuMF1MPhDT5484R4fLZfLsXz5cuY4fX19rF+//u0b5JJUqVKF+eeLChAIEQfvIdHVq1cX2xGbpwAhKCiIKw9CLly4IPrfn7Vr14q6HiHqVlhYCF9fX+a4SZMmoVy5csxx9vb2cHFxYY4DgBkzZqBKlSpYuXIlFi9ezLXGG0VFRZg8ebLSxbyEqApvxzhFJCcn48SJE5KtT4gYCgsLmSc4Ghsbw9XVlXmvx48fY8iQIaIVsWqay5cvY8eOHVyxYhYgAICNjQ3T9ampqUhISBA1Bxaenp7Iy8tTy95UgEA0wYkTJ5gPkbm5uXG9b1AF1g6tdDiKaKqXL1/in3/+YY7ThAmFANCrVy80bNhQtPUCAgK4prV8KD4+Hi9fvmSK0eUDs0Qxn3/+OXr16sUcx/MzTIim+vfff9GzZ09kZGRwxS9YsEDkjDRLzZo14e3tzVys1KRJEwwYMOC9r/Xu3Rvx8fEYP378exNqmjVrhnnz5uHChQui/o4tjZ6eHvOkMbrHJoSwogIEQgghOql9+/ZM1yclJSEpKUmibEhZ4OnpKdparB/iv3Hq1Clcu3aNOW7ChAlo2bKlwtezXAu87qqgzo54hOgCPz8/+Pv7M8d17NixxC5P3bp1U6j46F1UgEB4bd26VfQ1T548iZiYGNHXJURdzpw5g7S0NKYYQ0NDTJw4kXvPRYsWMcfY2Nhg3rx5b/88f/58tGvXjjsHAIiMjERgYKBSaxCiCsnJyaIcoCnJ/PnzqSCHaLSIiAjmA2BdunSBh4cH135hYWHv/d7RJTyNJACgdevWqF27tqi5WFtbM8fwPsNSlkwmw7Zt29SyN/D6Z4AQdeO5H3F3d5cgE3GwFiAkJycz/y4iRBV27tyJ3NxcppgGDRqgVatWEmXERl9fH1OmTBF1zYULFyq9Bs/0gw4dOii9L9F+Y8eOZY7x9/enzzWJTnj58iXc3d3x6NEjrvguXbqIXviuiXr27InffvuNKWbu3LmfbP7WsGFDbNu2DdnZ2Xj16hWePHmC2NhYLFu2DBUrVhQrZYWx3mPfvXtXZxtAEEKkQQUIhBBCdJKDgwNzDM/DK0IAIDs7G97e3qKtx/vh7a5du5hjDAwMMGfOHKaYFi1aMF3/6tUr7gcbhBAgJycH06ZN44pdsGBBiQUGVatWRdu2bZnWfPToEZKTk7nyIWVXRkYGDh48KMnaUhQ2EKIuPD8nQ4YMYZ5Q9a4uXbrgP//5j8LX29vbw9fX973Oqfr6+ti2bdt7nZ14rFu3Tql4QlQhMDBQ8uKAa9euYd++fZLuQYgyTp06xRzj6uqK3r17c++5evVqHDt2jDteEz18+BB+fn5csTNnzhQ5G8DW1pY5Rl0FCFevXlVrM5l79+4hPT1dbfsTUlRUxFW827NnTwmyEQfr4SgA3JOECZGCTCbDli1b8MMPPzDHfn6hEo4AACAASURBVPnll8xNYqQ0duxYGBsbi7ZeZGQk7ty5o9QaFy9eZI6xt7dXak+iGzw8PFCtWjWmmAcPHlAXcKL15HI5xo0bh6ioKO41eBrXaKvvvvsOkyZNUujanj17ljplRxAEVKxYETVq1BAjPW6s99iFhYW4e/euRNkQQnQRFSAQQgjRSTwPlcLDwyXIhJQFhw4dErXbUmhoKAoLC5lisrKycOTIEea9Ro0ahXr16jHFsBYgAKDu1IQoYdWqVbh//z5zXLt27eDm5lbqdd27d2deOzIykjmGlG1eXl7IycmRZO0jR45QRyqiEwoLC+Hr68scN378eKX3XrZsGYYPH17iNbVq1cKaNWtw/vx5NGrU6KPvt2nTBj4+Pszjqt8VFBRE941E44WFhalkn3nz5iEvL08lexHCKjg4mDnG1dUVjRs3hqurK/e+X3/9NVJTU7njNc3WrVu5CpqsrKwwdOhQ0fOxs7NjLiYMDQ1Vy714aGioKOuYm5tjzpw5TMWYb9AUBKJOly5dwrNnz5hi2rdvj+rVq0uUkfKaNGnCHEOHQ4mmkMvlmD59OvfkgGHDhomckXLMzc0xevRoUdc8fPiwUvGsTeTKly8PGxsbpfYkuqFcuXJc985STz4kRGr79+9XqinU8OHD4eLiImJGmk0QBGzcuLHU3+W2trbYuXOnRhUOloSKfAkhUqMCBEIIITqpXbt2zAdfeLpnEAIAO3bsEHW91NRU5jX9/PyQlZXFFCMIAmbPns0UA/AVINy8eZM5hhAC3L9/H7/++itXbGnTD97gOQB07do1npRIGSWXy7F9+3bJ1k9KSuIq0iFE05w9e5b5UGWNGjXQqVMnpfc2NDSEl5cXAgMD4ezsjCpVqgB4XXTQr18/HDhwAImJiZgxY8YnR0u/8cUXX+DEiRNo1aoVdy40BYFoOp4ChNq1azPHJCYmYsWKFcxxhEgtOzsbFy5cYIqpVq0arK2tAQCbN2/m7qibkZGBhQsXcsVqmry8PPzxxx/McYaGhvD29oahoaHoORkZGcHR0ZEpJiUlBXFxcaLnUpozZ85wxxobG6Nbt27w9PREUlISVqxYAWdnZ+Z1bty4wZ0DIcriKcJxd3cXPxER8RyOogIEoikOHjyIDRs2cMU2b94cLVu2FDkj5S1duhS1atUSbT1lChCysrIQHR3NFNO2bVtJ7peIdvLw8GCO8ff3lyATQlQjJSUFU6dO5Y5v0qQJfv/9dxEz0g4GBgbYuHEj9uzZg/bt27/3vbp162LBggW4cOGCUtOIVY2KfAkhUqMCBEIIITrJ1NSU+YHd1atXkZ+fL1FGRFfduXNHqQ89i7Nw4UKmqQre3t7MewwePBhWVlbMcc2bN2eOoQIEQvj88MMPyM3NZY5r1aoV+vTpo9C17dq1Y16fChAIi4iICMkP50jxu5gQVePpSDVw4MASCwJYubm54fTp00hLS0NGRgYeP36Mw4cPY/DgwQp/cO/q6opr167By8uLK4e9e/fqVHdrolvS09OZP4Rr3bo1jh49yrXf0qVLERISwhVLiFTOnz/P/PysW7dubxuFWFlZwcvLi7tb4M6dO5Gens4Vq0kOHTqEp0+fMsctW7YMtra2EmT0Gs9BfFW/ThUVFeHcuXNMMWZmZsjMzMSLFy+QkZGB4OBgTJw4ESYmJgD43hfHxsYyxxAiFtZO4IDmFyBYWFjAyMiIKYYORxFNIJfLsXTpUu74L7/8UiO7KFepUgU7d+5kno5UnPDwcCQnJ3PFXr16FUVFRUwx9vb2XHsR3eTk5IQKFSowxZw7d47pc1pCNIVcLsfkyZO53zcbGxvj0KFDqFSpksiZaQdBEDBixAhcvnwZjx49QkxMDOLi4pCYmIiff/4Z5cqVU3eKTHjOgtA9NiGEBRUgEEII0VkODg5M1+fm5lLnKsJs586dkqz79OlTrFy5UqFr09PTcfz4ceY9Zs6cyRwDvB6/W6dOHaYYKkAghF1AQACOHTvGFbty5UqFP7gyMzNDw4YNmdaPjIzkSYuUUVJOP3iDChCItnv+/Dl8fHyY4wYPHixBNq8/aDEzM1Mqfvjw4QgPD2eO5e0ITYgqsHZ9B14X9tja2qJ3797MsTKZDMOHD0dKSgpzLCFS4Tls/uHUtX79+nFPetOV3xNbtmxhjmnXrh1mzZolQTb/TxsKEK5fv858GMzZ2RkmJiaoVKnSJ4sqq1Wrhrp16zKtqY7JD4QArw+VsRYgfPbZZ1yFNqqkr6/PfEAqPj5eomwIUVxkZCRiYmK447/88ksRsxGXm5sbrl27Bjs7O1HW4y3MDg4OZo7p0KED115EN5UvXx7dunVjiiksLERQUJBEGREinYCAAKWmzuzYsQMtWrQQMSPtVadOHbRo0QJNmzbVyGJBRRgbG8PCwoIphgoQCCEsqACBEEKIzuLpbnHx4kUJMiG66uXLl9i6datk669duxaPHj0q9bpDhw6hsLCQaW1LS8uPRgeyYH3wEBsbC5lMxr0fIWVNbm4uvv/+e67Y/v37w83NjSmmbdu2TNc/ePAAz58/Z4ohZVNmZib27dsn+T5UgEC03a+//oqMjAymmBo1aqBz584SZSQOe3t75g94AeDvv/+WIBtClBcWFsYc4+joCOD1z/mbDvAsnjx5gkmTJjHHESIVnp+DDwsQAODHH3/E6NGjuXLYsmUL83MQTfDm0LCTkxPXf8dp06ZxvY6waN++/dupAIo6c+aMSp/58Nz7Ozk5lXoN68TP2NhYyOVy5lwIUdajR4+YixO7du0q+euHGJo2bcp0fUJCglb+PiC6xdfXlzvW1tYWlpaWImYjvqZNm+LixYu4c+cOdu/ejc2bN3MXEvAeiPX392eOoQIE8iGepgABAQESZEKItJYvX84dO3fuXI0ujCN8WO+xb926Re91CSEK0/wnDYQQQggnKkAgUlu7di33+EJF5ObmYu7cuaVe5+3tzbz28OHDlarUZy1AyM7ORmJiIvd+hJQ169evx927d5njKlSogN9++405rk2bNswx169fZ44hZY+Pjw8yMzOZYsqVK8d8+OfBgwd4+PAhUwwhmiIpKQkbNmxgjhswYAD09fUlyEhc06dPZ465du0asrKyJMiGEOXwHBju2LEjAKBly5YYM2YM175HjhyhiY1EI+Tn5+PKlStMMQ0bNkT9+vU/+rogCNiyZQtat27NnMfDhw/h5+fHHKdOjx8/Rrdu3WBvb891gL5atWqSTT56l6GhITp16sQUk5aWptLJl6GhocwxXbt2LfUa1vcgmZmZSEpKYs6FEGWxTj8AtOcgbpMmTZiuz8/Px4MHD6RJhhAFKVOAMGLECBEzkY4gCGjUqBFGjhyJb7/9Fn379uWa0nb69Gnm5gvJycnM03hr1KiBevXqMcUQ3efu7s4cc/z4cTqES7RKQkIC1/ROABg1ahSWLVsmckZEE7AWIGRkZODp06cSZUMI0TVUgEAIIURnWVlZwdzcnCkmPDxcomyIrklLS8OaNWsk32fv3r0lfrj/6NEjnD17lnndYcOGKZMW1+hFZcYQE1KW5Obmcr++zJ07l3mUJsBXgMD6wQ8pm7Zv384cM2DAAAwYMIA5jqYgEG21ePFi5ObmMsep4hCiGHr16gUrKyumGLlcToeticbJy8tjPnjdtGlTVK1a9e2flyxZggoVKnDtr4r3n4SUJjIykvl3lrOzc7Hfq1ChAg4ePIhKlSox57Jx40bmGHWJjY1F27ZtERISwr3GuHHjYGRkJGJWxVNkWsCHVDW9qKioCOfOnWOKMTc3h7W1danXsRYgAK//vyVE1S5fvswcY2dnJ0Em4mM9HAW87tBKiLrExcVx/x387LPPMG7cOJEzUp3q1aszT2UsLCzE8ePHmWJYrweAbt26KdWAi+imevXqoWXLlkwxycnJVOhGtArPxBjg9evm9u3b6bVTR9E9NiFESlSAQAghRGfp6ekxT0G4f/8+0tLSJMqI6JIVK1Ywd3TmNXPmzGI7bOzfv5+5+4atrS1zN6kP8RQg8Hw4RkhZdODAAaSmpjLHNWrUCLNmzeLas23btswx165dK/Wau3fv4uDBg/jll18wdepUTJ8+HStXrqRO9WXEhg0buF77x48fr1CH0g9RAQLRRvfu3cPOnTuZ42rWrIkuXbpIkJH49PT0MG3aNOY4KnQjmubq1avIy8tjinF0dHzvz7Vr18aMGTO49j9+/DhkMhlXLCFi4ZkC8uHPwYcaN27M1WUxJCREKxodxMbGwtnZmatL8BuCIGDixIkiZlWykopGirN9+3YUFBRIkM37oqOjmTsnd+nSRaGpUc2aNWPOJy4ujjmGEGWxTkDQ09ODra2tRNmIiw5HEW1z+PBhrjh9fX3s2LEDFStWFDkj1erfvz9zDGtnbp7DtL1792aOIWUDz98N3m7yhKjDP//8wxxTpUoV7N27F+XKlZMgI6IJeO6x4+PjJciEEKKLqACBEEKITmMtQABeHyogpCTJycnYvHkzVyxPt8tz587hyJEjn/yet7c383rDhw9njvkQT1e4PXv20IEZQkohl8u5O3lu2LAB5cuX54qtUaMGatWqxRRTXAGCXC7Hzp07YWtri8aNG2PIkCGYP38+Nm/ejHXr1mHOnDmoX78+Bg4ciPv373PlSzRbRkYGunfvznXguFGjRnBycoKDgwMMDAyYYqkAgWijP/74g+v+6Ntvv1XoIJ2m4PmAlwoQiKZIS0uDt7d3qYeoP+VTMXPnzuX64C8tLQ1RUVHMcYSISYoCBAAYPXo0TE1Nmddeu3Ytc4wqxcfHw8XFBU+fPlVqHQ8PD9SvX1+cpBRga2vL/P9HcnIy/Pz8JMro/4WGhjLHKFrczFOAQBMQiKoVFhYyf37RsmVLmJiYSJSRuHia5tDhKKJOvr6+XHEHDx5Enz59RM5G9XgKEFiK9/Lz8xEUFMS0vp6eHnr27MmaFikjevXqxRwTHh4uQSaEiO/ly5dcn5Fs2rQJNWvWlCAjoil47rGp2J4QoigqQCCEEKLTOnTowBxz5coVCTIhumT79u3MnS8BYP78+Vi6dCnXngsXLvxo0kFcXJxCHcjfJQgChg4dypXDu0xNTWFhYcEUk5iYiNOnTyu9NyG67NKlS4iIiGCO69u3L9zd3ZXau02bNkzX37p1C9nZ2e99raCgAJMnT8bYsWNLPDgql8vh6+uL5s2bY9myZSrplElUIysrC71790ZwcDBX/Lhx46CnpwcTExO0b9+eKfbOnTt4/Pgx176EqAvPQb1q1apxFfioU7169VClShWmGNb7XEKkcOjQITRr1gxfffUVV/ynDl4bGxvjwIEDMDQ0ZF6P9fANIWKSy+XM3T+rVasGS0vLUq8zNTXF6NGjmXPau3cvkpOTmeNUISMjA+7u7kpNPnhj+vTpImSkOAMDAzg5OTHHeXp6ip/MB3ieKyn671KlShXmgzdUgEBU7ebNmx89iykNz2ck6lKxYkXUqVOHKYYmIBB1SUxM5GpolpiYyHVwXxPVr18frVq1YophKRo6d+4cXr16xbS+g4MD8/MHUnZ06NCB+b04TUAg2iIoKAiFhYVMMZaWlvjyyy8lyohoipo1a6JSpUpMMdQciBCiKCpAIIQQotPatWvHHMNz8JOUHXK5nGvqgLm5OWbOnImpU6eiYcOGzPExMTG4ePHie1/bt28f8zpOTk6oXbs2c9yn8Hx4tWPHDlH2JkRXbdq0iTmmfPnyWLdundJ7t23blul6mUz2URfe//73v9i6davCa+Tm5mLBggVwc3NDeno60/5EM23YsIH7Qxl9fX18/fXXb/+saKfSd/EWPhCiDsnJyYiJiWGOmz9/PvMHBuomCALz75mYmBiuol9CxLJx40YMGjQIaWlpXPGfffZZsQevra2tMWvWLOY16fccUad79+4xH6bv2LEjBEFQ6NopU6Yw51RQUMA9QU5Kcrkc48aNw71795Rey8HBgasYQFlDhgxhjgkKCkJCQoIE2byWk5PD/DpoZmbGdDCSdQpCbGzsRw1DCJHSpUuXmGPs7OwkyEQ6rB1aqQCBqEtxU6tL8sUXX6BevXoSZKM+rJ/DJiUlISsrS6Fr/f39mfPhmcBIyo7y5cszP5+KiopCZmamRBkRIp5//vmHOeb7779X+D070V6CIDBPY42IiGAuaCGElE1UgEAIIUSnVatWjXlEOU1AICWJioriGus8e/ZsmJubw8jICL/++ivX3u8+0OYthBg+fDjX3p/St29f5hgfHx9Ruv8RoouePHmCAwcOMMdNmzYNDRo0UHp/1gkIAN7r8pWSkoLly5dz7R0SEoIRI0bQwQ0tV1BQgM2bN3PH9+nTB7Vq1Xr7Z54ChGPHjnHvT4iq8Rwkrl+/PiZNmiRBNtJj/YC3sLCQq0CDEDHcuXMHs2fPVmoNR0fHEj/E/fnnn5nXPHv2LHJzc5VJixBuYWFhzDGfmgJSnCZNmqBHjx7Me/z+++8adyDI09MThw4dEmWtuXPnquVAyMCBA7m6B7MUpLM6ffo0cnJymGI6deoEfX19ha9v3rw50/rPnz/H06dPmWIIUcbly5eZY7RpAgIA5sNRaWlp3AWjhCgjJCSEOWbAgAESZKJeVlZWzDGKFCwWFBRg//79zGtTAQIpjYODA9P1RUVFdHaAaDyZTIaAgADmuD59+kiQDdFErVu3Zro+OzsbcXFxEmVDCNElVIAgIkEQjARBsBYEoZcgCEMEQRj9v//t9b+vl1N3jppKEISagiB0EQThC0EQRgiCMPJ//9xFEAS2mbeEEPKB9u3bM13/+PFjPH78WKJsiLbjeeBZo0YNfPfdd2//PGjQIHTs2JF5naNHj7795ytXruDu3btM8YaGhhg4cCDzvsXp378/V/ddCwsL5rG5hJQF+/btQ0FBAVOMvr4+pk6dKsr+PAUIfn5+b/95x44dSh38CQwMxN69e7njifpdvnwZ//77L3f8+PHj3/uzo6Mj02Eh4PXfIzqYSbTFyZMnmWMWLlwIIyMjCbKRHmsBAkCjnon6zJkzh/mQ64c6depU4vcNDAzQr18/pjVzc3O5Jw0RoiypCxCA190XWWVkZODPP/9kjpNKVFQUpk+fLspaNjY2ajtEV6FCBYwePZo5bseOHQp3NWbF09GTdXoEawEC8HoKAiGqwjoBwcTEhOvvtTqxFiAA4GoYRIiybt68yXS9gYEBPDw8JMpGfVinlgCK/cweOXKE+bPaOnXqwNramjkfUrbwfD4bHh4uQSaEiCciIoK5MLply5awsLCQKCOiaVjPTAF8xc+fUlhYiOTkZNy4cQO3b99GRkYGNaQjRIdQAYKSBEGwFwThV0EQbgDIAhAFIADAfgA7//e/Af/7erYgCDcEQVghCIK92pLWAIIgmAuC8I0gCIcEQXgGIBnAGQBHAOwBsPt//3wGQLIgCOn/u3asIAjm6sucEKKNWMd/Aq/fpBHyIblczlWAMH/+fJiYmLz9syAIWLt2LfM68fHxbx/M8nRJd3d3R+XKlZnjimNsbIxhw4Yxx+Xl5cHFxQUZGRmi5UKILjh+/DhzTP/+/VGnTh1R9q9fvz7MzdlutYODg98+1OR5ffzQkiVLaKSnFjt79ix3bN26deHm5vbe10xNTWFra8u0TlZWFk6dOsWdR1kQHx8PLy8vrFmzBvv378eTJ0/UnVKZJJfLmScg6OnpoX///hJlJD2eQjcqQCDq8O+//743fY6XIj+v3bt3Z143KCiIJx1ClMZagFCuXDnme7levXpxHTyVsus+C7lcjsmTJyMvL0+U9dQ1/eCNiRMnMsdkZGRgz549ouaRlpaG8PBweHp6MseyFnA0a9aMeQ8qQCCqkp2dzfz3rV27dsyF/epGBQhEG+Tn5+P+/ftMMU5OTlzThTSdVAUIPFNW3d3d1XrvRLQD6wQEANQIgGg8nmJtXSyKI8Wzs7NjjlGmACE9PR1LliyBg4MDjIyM8Pnnn6N169Zo0qQJKleujE6dOsHPz48KEQjRAVSAwEkQhKGCIFwFEA5gNgAbAKU9wdH/33VzAIQLghAhCMJQaTPVLIIg1BEEYTOAJAB/ABgAQJGTkFX+d+0OAEmCIGwWBEGck1aEEJ3HU81LBQjkU65evYp79+4xxRTXMa5Dhw5c0wjeTEHgGe87fPhw5pjSfPPNN1xxERERGDx4MHO3d0J0lVwux9WrV5nj3p2uoixBEJgPCBUVFeHgwYO4desWoqKilM7h7t27aN68OfOEF6IZlClAGDt27CcPRfA8BH93YhD5f3Fxcejfvz+aNm2KESNGYNasWfjyyy9Rq1YtjBkzBs+fP1d3imVKTEwMc/GHnZ0dc6GYJmncuDEqVqzIFHPt2jWJsiGkeNu3b0dRUZFSawwbNgwNGzYs9TpXV1fmtVmLlwgRw8uXL5m7/Nra2qJ8+fJMMXp6epg5cyZTDPD6ALgmvIc4ceKEaIeTbGxsMGjQIFHW4mVlZYVu3boxx61fvx4ymUzp/UNDQ2FnZ4fPPvuMq1Nto0aNmA8y83SKj4uLY44hhEd0dDTzz1aHDh0kykY6PIeZb926JUEmhBTv/v37zO8ZunTpIlE26tWwYUPmQqfSChCio6Nx5swZ5lzUNTmKaJc6deqgbt26TDHh4eF0SJZoNJ6JhfSaWbY0b94cxsbGTDFXrlzh2uvs2bNo3bo1Fi9ejIsXL37yPcyFCxfQt29fzJkzh15fCdFyVIDASBCEpoIghALwAcA+O/59tgB8BEEIEQSB/WmKFhEEQU8QhJkA4gB8C8CklJCSmPxvjThBEGYIgkB/jwkhJWrblv3lmvdmmug2nu7eHh4exR60+uqrr5jXO3bsGDIzM3Hjxg2muIoVK0rSyaBdu3Zo2bIlV2xwcDC+++47elNJCICHDx/i2bNnTDE2Njbo3LmzqHnwHIKbOnUqFixYIFoOCQkJaNKkCebPn09FSlqksLCQ6yE3AFSpUgXff//9J7/Xr18/5vWOHTsmyoEnXSGTybBhwwa0bdu22I7eu3btQtu2bZGenq7i7MqukydPMsf06NFDgkxUR09Pj3kKwo0bN2gyDlGpgoICbNu2Tak16tSpg/Xr1yt0raWlJerVq8e0/tWrV+n1mqgcT2dpR0dHrr1GjBiB6tWrM8edO3eOaz8x/f7776KsY2Jigv3792tE1/LJkyczx9y6dUvpaS1Lly6Fs7OzUs9oPTw8mLsgV69enbk7NU1AIKrCU5zL05xJ3WrXrv3eNGFFUCEQUbWEhATmGCsrKwkyUb9y5copVHz9rtLuLbds2cKch7m5udY/NyGqwzoF4dmzZ7h9+7ZE2RCiHJlM9n/s3XdcFNcWB/DfLE2qIEhREUGsoGDvPXYJYo2xR01sUWM0McYWjSVqoomamKjJs0RN7L0XLBgLWEFFsYEdpAvS5v1BNBZg996d2Z3dPd/Px897cffcexxgmJ255x7mDc5KlCiB+vXry5QRUSJzc3PmTeguXbqEjIwMppibN2+iXbt2iIuL0+j9c+fOxZw5c5jmIIQoCy3cZiAIQhcAZwE0k3jo5gDOCYKgvi+3ARIEoTiAnQDmA2Db6q5odgC+B7D93zkIIaRAxYsXZ94159y5c7QomrxBFEX8/fffzHE9exbe7KhNmzbMuwGGhYVh586dzLvrhISEMFe1a0IQBHz00Ufc8b/++qvGi3MIMWYRERHMMQMGDJC8pXSPHj244jZu3ChpHrm5uZg5cyYsLS3h6OiIbt26Yfv27fS7WcEuXryI1NRUrtgFCxYUusjH398f3t7eTOM9fvwYp0+f5srF2Dx//hydOnXC6NGjkZmZWeR779y5g/79+9PPmY7wLMpr3bq1DJnoFmtxeGZmJu1mSnRq27ZtePjwIXe8paUlNm3ahJIlS2r0fkEQmAtARVHkLvojhBfPIjueHesBoFixYlyd3vRdgBAbG4udO3dqPY6rqyvWr1/PvHO/XN5//314eHgwxy1cuJB7zvXr12PKlCnc8S8FBQUxxwiCwNwFgbU7CCG8Lly4wBzDushICVQqFfPzHPrMQHSNZyGysRYgAOydS65fv17o/aekpCSsXr2aOYePPvqI+XkbMV2sBQgAJOt0RojUYmJikJyczBTTrl07mJuby5QRUaq6desyvT83N5epCFoURQwePJi5aGHy5Mm0QSwhBowKEDQkCMIIABsh7QL619kB2CQIwnCZxtcLQRBKAggD0F7GaToCOCkIgouMcxBCDFzt2rWZ3h8fH4+7d+/KlA0xRBEREbh37x5TjJ2dHTp06FDo67a2tlyLTSZOnMgUA0CW7gcv9enTBxYWFtzx48aNow+VxOTxFCDUq1dP8jx8fHwUt+tJcnIyNm3ahODgYHTu3Jm5UwTRDd4FXytXrkS/fv0KfV0QBAQHBzOPu23bNq58jMmLFy8QEhKCPXv2aByza9cuOnY6kJmZidDQUKYYe3t7Wc77usbTnW7Hjh0yZEJIwbTdvfyXX35hfpjHU1xEn5+IrvEUIFSvXp17vmHDhsHS0pIpRt8FCMuXL+fuwlW8eHHUrVsX48ePR3R0tKz3cFhZWFhg+HD2x1Z79+7lWpwZHx/PNd/bHBwcuDsGVqlShen9T548QXx8PNdchLBgLUBwcHBAuXLl5ElGZqxFWLdu3VJbdE+IlHh+x1WoUEGGTJSBtQAhLS2t0MLvxYsXIz09nWk8QRC4ujYR08VTLM16L48QXeG5R9S4cWMZMiFKx9Md7cyZMxq/NzQ0lOtcmZubi/79+zMXLhBClIEKEDQgCEJ/AIsASLu9aAFTAVgsCELhqy8MyL9dCfYBYNsuho8fgP3UCYEQUhiei2l6oE9et2vXLuaY999/H9bW1kW+h2dR5e3bt5ljeHce1ETJkiUxePBg7vjc3FwMHz6c+0E9IcaAtQBBEAQEBATIkkuvXr1kGVcK27dvh7Oz+7bHnwAAIABJREFUM7755hts3LgRhw8fxpMnT/SdFgFw7Ngx5pj9+/cXWXzwUufOnZnHXrduHXO3IGMzZswY7N+/nzlu8uTJ9DtZZmfOnGFeHNOiRQutCj6VgqcA4ccff6TFREQnoqOjcfjwYe74cePGcXWHa9myJXMM3a8gusa6yM7c3BxeXl7c8zk7OzPfy7tx4wYePXrEPac2cnJysHz5cua4MmXKID4+HklJSTh9+jTmzp2L4sWV94jjk08+gZWVFXPcunXrmGO+/vprJCYmMse9rW3btsxFLC+xdkAAqAsCkV9ubi4uXbrEFBMYGCh550xdYS0EysvL4yqWI4QX6/ebh4cH7Ozk2udS/1gLEID8LghvS09P5+qi1L59e/j6+jLHEdMVGBjI3DHjwIED1DmWKNK5c+eYY3jWzhDDx7ppCsBWgLBo0SLm8V+6evUqJk+ezB1PCNEfKkBQQxCEOgCWQbPigzAAIwHUBFACgMW//1sbwCgA/2gyJYBl/85r6P4HoIYG70sC8AuAIACeAKwB2AAoC+B9AL8C0KRfVA0Af/AkSggxfjwfovS9cxpRlt27dzPH9OzZU+17goKCZH8QVLZsWZQpU0bWOaZPn47y5ctzx587dw4bNmyQMCNCDAtrAULlypVha2srSy49evSASqXsj4rTpk1D9+7d0apVK7i5ucHPzw+rVq2iBwB6IooicwGCo6MjWrVqpdF7GzVqhBIlSjCNf+/ePa7f3cYiNDQUS5cu5Yq9cuUKZs6cKXFG5HU8D4V4dkhXosqVKzM/4H38+DFWr14tU0aE/OePP/hvK86aNQtz587linV1dWVesHP27Fm67iE6xbrIrnz58jA3N9dqTp7d60+cOKHVnLx27tyJBw8eMMetWbMGzs7OMmQkrZIlS6J3797McazX4+fOncOyZcuY5ymINl0k/P39mWOuXLnCPR8hmoiOjmbeEbRGDU0e0SoTawcEIH/REiG6wlqcWbFiRZkyUQaef19BBQi//fYbEhISmMcaMWIEcwwxbZaWlqhduzZTzIMHD3Dt2jWZMiKEH+u9ZgsLC1SrVk2mbIiSlStXDi4uLkwxmm6CcvfuXWzdupUnrVd++OEH5uflhBD9U/aqEj0TBMEBwF/ILyQoyg0A74mi2EgUxSWiKJ4XRTFRFMWcf/83XBTFRaIoNgDQFkCMmvEsAfz17/wGSRCEUQDUbVMpAlgIwFsUxeGiKO4URTFOFMVMURQzRFGMFUVxhyiKQwF4I78LhTohgiB8qmX6hBAjFBgYCDMzM6aYgwcPypQNMTRPnz5lqu4G8ltct23bVu373NzcUL9+fd7UNNKoUSNZxwcAFxcX7Nu3D97e3txjTJw4EVlZWRJmRYhhePjwIfNOnTw7SGvK3d1d44XhShEVFYX+/fsjJCQEaWlp+k7H5Fy9epX54WCTJk00LnQxNzdHUFAQc14///wzc4wxeP78uVadiQBgypQpGDRoEJKSkiTKiryOpwChTZs2MmSie+bm5qhXrx5z3Lx580y+qwmRV25uLlatWsUV++DBA3z11VdaFZazbprw7Nkzrs54hPAQRZG5AKFChQpaz8tTgKDNZiIXL17EhAkTUKtWLQQEBKBly5b46aefkJqaqjaWZ9F8w4YN0axZM55U9WL06NHMMWfPntW4Y11eXh5GjBghSXGVIAjo0KEDdzwVIBAlunDhAnNMYGCgDJnoBmsHBIAKEIjuPH/+HHFxcUwxxl6AIEUHhBcvXmD+/PnM4/j4+KBdu3bMcYQ0b96cOYbWDhClyc3NZV6wXb16da4Od8TwCYLA3AXh5s2bGt2D/OWXX7TurC2KIqZOnarVGIQQ3aMChKJNR/7C96IcBFBHFMVDmgwoiuJ+5HdEOKLmrd4ApmkyptIIglAawCw1b8sC0FUUxc9EUVS7ouHfQo5RALoDyFbz9lmCIJTSLFtCiKmwsbGBn58fU8zVq1dx//59mTIihmTv3r3MD0DbtWun8Yf34OBgnrQ01rBhQ1nHf6l8+fIICwtDQEAAV/ytW7fw66+/SpwVIcrHs5uDnAUIANCrVy9Zx5fLtm3b0LZtWyQna9JAjUiFtfsBwL6gjOd35b59+3Dr1i3mOEM3adIk3Lx5U+txfv/9d9SuXRtPnz6VICvyuvDwcKb3u7m5SbKQUym6devGHHPjxg2td1AipCj79+/n2r08OjoaHh4eWs/P07VR0x3ICNFWQkICc1GiFL+3GjZsyFzYw9MBITc3F7Nnz0atWrXw3XffISIiApcuXcKRI0cwevRoeHp6YtmyZYXeF0pLS8P+/fuZ5x06dChzjD5Vr14dLVq0YIoRRRH79u3T6L07duxg3vyjMG3atGHe1fF1bm5uzJ0pqACByM3UChAqVKjAvKEUFSAQXeG552JMn+kL4ubmBgcHtr013y5AWL16NddnshEjRii+my9RJp5uowcOHJAhE0L4Xb9+Henp6UwxrN0/iHHhuQe5Zs2aIl/PyMiQrJvhrl27cPfuXUnGIoToBl2JF0IQhKoA1PVqOwUgWBRFptUt/y64DwKg7m7qp4IgsG/xoH/zANgW8boIoJcoiltYBxZFcSMAdb1+7f7NgRBC3sCzq9ihQxrVlxEjt2vXLuaYjh07avzerl27Mo/PQlcFCED+zumhoaHcRQjTpk3Dw4cPJc6KEGVTYgFCly5dYG1tLesccgkLC0OrVq242nUTPnv37mWOadq0KdP727Vrx/wgUxRFjBo1iinG0C1fvhwLFiyQbLyYmBiEhITQzvMSSk5ORnR0NFNM7dq1tdpZXWn69euH4sWLM8fNmjVLkl2RCSnI77//zhzTpk0byRYSUQECUTLW7geANIvsHB0dUb16daaYCxcuICUlhSlm1qxZmDhxYqHXO8nJyfj444/Rtm1bxMbGvvP6yZMnkZOTwzSnk5MTV0Gevg0cOJA5Zvfu3Rq975dffmEeuzBffvmlVvGCIDB3Qbhy5QpdpxBZnT9/nun9FhYWqFq1qkzZyM/S0hLly5dnirl27ZpM2RDyJtbP9IDxd0AQBIG5C8Lbx1Hd4saCODo6YsiQIcxxhABA/fr1YWNjwxRz9OhRZGer2y+VEN3h6bTLcw+KGA/WDggAsGrVqiI/7/7+++949uyZNmm9Iooili9fLslYhBDdoAKEwk0FYF7E688A9BRF8TnP4KIopgPoAaCorYPMAUzhGV9f/i3c+EDN274XRXEz7xyiKG4AsFDN23oJglCZdw5CiHHi2cmAWimSnJwcjXdre0kQBKaWr76+vnjvvfdYU9OIra0t8wN7bRUvXhw7duzg2gn02bNn6N+/v9Yt+ggxJDwFCDVq1JAhk/8UL14cY8aMkXUOOYWHh6Ndu3bMO78QdsnJydizZw9TjI2NDXMRjbW1Nfr3788UA+QXEa5bt445zhDt2bNHlp10T548iSVLlkg+rqniOecb265UDg4OGDZsGHNcREQEV8ETIeokJCRg+/btzHFSLnQJDAyEuXlRt6LfRQUIRFf0VYAAsHfNysvLw6lTpzR+/4ULFzB9+nSN3nvgwAHUrl37nZ+9o0ePsqQIABgwYIBBFny3a9eOuShy3759aotZ09LScPjwYW1Se2XixInMnRoKwlqAkJSUxLVrMzEdqampuHjxIi5duoTr168zdW4URZG5A4Kfnx8sLS1Z01SUKlXY9ui7fv06Fc8TnaAChIKxFiDcvn0bL168AJB/jjx+/DjznKNGjYK9vT1zHCFAfrEb6+aFqampknXtIkQKPPeGjO1eM2HToEED5s5BN2/eLPRey7NnzzB16lQpUntlxYoVVOxFiAGhAoQCCILgA0DdVsSTRFF8d7sbBqIo3kV+oUNRuguC4K3NPDo2HkBRd6DvApgswTxfA4gr4nXh31wIIeSVZs2aMbftPXjwIO1eZeJOnTqFpKSi6gXfVadOHbi6ujLFjB49mun9mqpXrx7zQhYpeHp6YsuWLVy79R44cAALF6qrNSTEeLAuRvX19eXaOZrVl19+CV9fX0nHbNSokc4egJ87dw79+vWjgiaZbdu2DVlZWUwxDRs2hIWFBfNcvIvr+/bti61bt3LFGoqbN2+iR48esi22+Prrrwvc8ZewCw8PZ44xxodCo0aN4vp9MGPGDPp8RrSWmpqKLVu2YNWqVfj777/x3XffMf8uK1GiBIKCgiTLydramnmxa3h4OC2yIzrBs8hOXwUIAJgWr02bNo2pe8GTJ08QHByM+Pj4V3/HU4Dw8ccfM8coQcmSJZl3y0xMTERoaGiR7wkNDdV6cYGtrS2+//57fPvtt1qN8xLrORnI74JAyOtyc3OxZMkSlCtXDg4ODggMDERAQAAqV64MR0dHlC9fHjNmzFC7ecLDhw/x9OlTprkDAwO1SV0RWAsQMjMzcffuXZmyIeQ/rMWZKpUKPj4+MmWjHKwFCHl5ebh+/ToAICoqivkerq2trcl1PiXS49kcjjYvJErC2gGhWLFiBt0li2jPycmJq2h/5cqVBf79uHHjJO9I//DhQ+zcuVPSMQkh8qEChIKNAFDUCtUbAH6TaK6fAdwq4nUzAMMlmktWgiA4A/hQzdumiKKYqe1c/3aeUFe80UcQhBLazkUIMR4ODg6oV68eU8zDhw8RFRUlU0bEEGzcuJE5pmPHjswxHTp0kHyhL5C/yFNf6tWrh759+3LFTpgwocifvc2bNyMkJASOjo5wcHCAj48PvvjiC9y+fZs3XUL0Ij4+Hvfu3WOKYd05nlfx4sVx4sQJ1KpVS5LxWrRogSNHjiA8PFyyMdXZvHkzJk2apJO5TNX69euZY5o3b841V9WqVblic3Nz8eGHH+L8+fNc8xqCkSNHIi0tTbbx09LS8Omnn8o2vinhaYutq3OmLnl4eKBfv37McadOnZJsh2RietLT0zF06FC4urqiS5cu6N+/P3r27Il58+Yxj9W7d29YWVlJmh/rot709HRcu3ZN0hwIKQjrIjsrKyt4enpKMnfjxo2ZY8LCwjR6340bN7i6nzx8+BBffPEFgPyCJtYdJ8uXL4/KlQ23eXOHDh2YY1q1aoU7d+4U+vr+/fu583FxccHEiRMRHR2NsWPHcm2EURAqQNC/mJgYLFmyBN988w3mzp2L8PBwg9pgICkpCfXq1cPIkSMLXRR/69YtTJkyBX5+fkX+jPB8ljXFAgQAuHr1qgyZEPIm1uJMLy8vyT87KBFrAQIAXL58GQAQGRnJHDt06FA4OzszxxHyutatWzPHHDhwQIZMCGGXnZ3N3CUrMDCQa3MoYlx4uo3/9ddfyMjIePXfeXl5GDt2LP744w8pU3vl119/lWVcQoj0qADhLYIgmAHopeZtC0RRlGR7KVEUcwD8pOZtHwqCYAhfq54Aitq+7j6AdRLO9yeAx0W8bgmgu4TzEUKMAO1kQFgkJCRg+fLlzHE8D2NVKpUsC/v0WYAA5O9Sy3NzPTs7G5Mnv9s0KTk5GT169EDXrl2xdetWJCcnIzU1Fbdv38a8efPg7+9PFfHEoPC0R9VVAQIAuLm54fTp09i8eTPXjhgvNWvWDFu3boWFhQX8/f1x9uxZXL9+HT/88IOE2RZs9uzZXMVkRL2EhASuBy4hISHccw4bNowrLiMjAyEhIW/sVmssoqOjsW/fPtnn2bZtG/2OlQBrAUKpUqXg4eEhUzb6NW7cOK5FgjNmzJAhG2Ls4uLiUL9+ffz666/IzNR6bxQMHDhQgqzexFqAAPAVNRHCirUAwdfXFyqVNI8zSpUqxbxj8NmzZzXqDrJw4ULurjorV67E5cuXcfLkSeZOJLzFuErBc88LyO+GV9j3Es+1bN26dbFx40bExsZi5syZKFWqFFdehfHz82OOoQIEaTx9+hRdunRBhQoVMHLkSEybNg1ffvklateujbp16xrEAnNRFDFw4ECNu5/dvXsXrVu3RnJycoGvsy4sA4AaNWowxygNFSAQpWItQKhYsaJMmSgLz47aly5dAsBXgPDJJ58wxxDyNn9/f+aO9mFhYbh586ZMGRGiuUuXLjHf4+K590SMT5cuXWBnZ8cUk5ycjHXr1uHIkSMYN24c3N3dsWDBApkyzN+o4GWhIiFE2QxhUbuutQRQ1JPdTABrJJ5zJYCi+nyXAtBc4jnl0FvN6/8TRVG7PrqvEUXxBfKPXVHU5UQIMTE8OxlQAYLp+vHHH/H8+XOmGDc3N+7FwQMGDIC9vT1XbGHq168v6XisypYty90Gd/PmzW/s/hUfH4+WLVtiw4YNhcY8f/4cwcHB2Lx5M9echOjayZMnmWN0WYAAAGZmZggJCcHhw4cRHx+PRo0aMcUHBQVhz549cHBwePV3giCgYsWK+Oyzz5CXl4fffvsNbm5uUqf+yujRo5Gamirb+KZq8+bNyMnJYYqpVq2aVi1+O3fuzP29cvfuXfTs2ZM5Z6XbtGmTzuaaM2eOzuYyRomJiYiJiWGKqV27tkzZ6F+lSpXQpUsX5rjQ0FCNF3MRAuQvZGzdurVkC0MDAwNlWdTH8xCYp5iVEBaiKDIXIFSoUEHSHJo0acL0/rS0NLULUBMSErTapS8vLw8TJkzA0aNHmWO1KexWglq1aqFkyZLMcQ8ePEDv3r3fuRa/e/curl+/zjRWpUqVcPr0aXTt2hXFihVjzkUTTk5OKF26NFMMFSBo7/79+2jWrBm2bNlSYIFQeHg4atSogdOnT+shO82tWrUKW7duZYq5efMmli5dWuBrPAUIAQEBzDFKw9MthgoQiNwSExOZN7eQ+tpIqSpWrAhLy6L2qnwXbwGCjY0NypcvzxRDSEEEQWDevFAURfzyyy8yZUSI5ni6xBrzvWaiOVtbW3Tr1o05btCgQWjZsiW+//57PH36VIbM/iOKIkaNGsW9cQQhRHeoAOFdQWpe3yWKoqQrV0RRTAKwR83b1OWlV4IgOANooOZt62WYWl1HhUaCIJSQYV5CiIGqV68eczXv0aNHjW6hGlEvJSUFixYtYo5r3749905/Dg4O+PDDD7liCxIQEAAnJyfJxuP11VdfcbfCXb8+//LhwYMHaNasGSIiItTG5OXloW/fvlztyQnRNZ4CBH3uYufs7Izjx49j2bJlKFGi6MtsQRAwZswYbNq0CdbW1kW+b8iQIYiJicGSJUtk2b3qwYMHWLx4seTjmjJRFLFypbp68Hd98MEHWs1raWmJoUOHcscfPnwY3333nVY5KM2WLVt0NtfJkydx7do1nc1nbDS5jnmbsT8U+vrrr7ni1q5dK3EmxFglJSWhbdu2kp675Oh+AOTvts26kFbpCzCJ4Xv8+DHS0tKYYqReZNeggbrHDu9S97Px888/IyMjgzclAMDu3bu5riubNWum1bz6plKp0L59e67Ys2fP4s8//3zj73g6qrVp04Zrflb+/v5M74+MjEReXp5M2Ri/Fy9eIDg4WO0C8hcvXqBXr15ISUnRUWZskpKSMGDAAK7Yn376qcDvIdZ7nN7e3ihevDhXDkpib2/PXAhEBQhEbqyFmYDpdECwsLBg3nTk4sWLANgLEKpWrSpZxy1CWAsQAGDBggW4evUqUlNTaXEs0RueTTSN/V4z0Vy/fv30nYJaR48epQ73hBgAuip/l7qry10yzatuXPYtu3WrFQChiNfviaIo+fYvoiheAHC/iLeokN/VghBCAOTfAGN90JeWlsa1WIgYtl9++QVJSUnMccHBwVrNGxQkXc0hT8cPOTg5OXEv/F23bh1SUlLQunVrREVFaRz3shPCo0ePuOYlRBeys7OZF435+vrCxcVFpow0IwgCBg8ejPv372P79u344osv0KpVK3h4eMDe3h6urq7o3bs3QkNDsWDBAlhYWGg0rq2tLYYPH46lS5ciJycHM2bM0DhWEytWrKDFIBJavXo1VwFNz549tZ57/Pjx8PHx4Y6fNWsWHjx4oHUeShAbG6vz3a/37t2r0/mMyblz55hjatWqJUMmylGjRg106tSJOW7Dhg0GeU4XRZGK23UoJycHXbt2lbQwuVixYpIWjb/OwsKCudA0IiJCsQswiXHgWWQndQFCvXr1mGP++eefQl87e/Ysvv32W21S4ubr64syZcroZW4p8RYgAMD06dORnf1fo+z9+/czj6HUAoSMjAzcvn1bpmyM3/jx4zXucnX79m3Mnz9f5ozYPHnyBJ9++qlWm8E8ePDgneuWlJQU5i5qgYGB3DkoTZUqVZjef/XqVVoISmTF2rUHMJ0OCABQvXp1pvc/fPgQMTExiIuLY4rz8/Njej8hReG5thRFEVWrVoWDgwNq1qyJlStXIjc3V4bsCCnYixcvcPz4caYYR0dHVKpUSaaMiKFp1qwZvLy89J2GWp9//jnS09P1nQYhpAhUgPAaQRA8AKi7k8FeQqgZddu8+AmC4C7T3FJopeZ1uY6bJmOzlywTQowaz6JsnpbqxHBlZWVh4cKFzHGlS5dGhw4dtJq7RYsWkrWPV0oBApC/43Xfvn2Z4y5duoTGjRszFR+8FBsbi169etFDJ6JY58+fZ951s1GjRjJlw65YsWIICgrCd999h4MHD+LBgwdISUnB48ePsWbNGjRp0oR7bDMzM0yaNAnnz5/H559/jjp16mi9e19MTAyOHTum1Rgk3+PHj/HZZ58xx9WuXVuS9ui2trZYsWIFzMzMuOKfP3+OX3/9Ves8lGDr1q1ccYcOHcL333/PFbts2TKcO3cOERERBrkAXJ+oAKFgkyZNYo6JjY3FmTNnZMhGHpGRkRg8eDC8vb1haWkJNzc3NGrUCNOnT0dERARdr8pkyZIlXO3oizJixAhZi0Hr1KnD9P68vDzmB86EsIiOjmaOkXqRnb+/P2xsbJhiCiv0jo+PR9euXZGVlSVFasxatGihl3ml1rZtW1hZWXHF3rp161UntdzcXOZdOy0sLNC8eXOuuVmxFiAAwJUrku/DZRI2bdrE3In2xx9/RGJiokwZsbl+/Trq168vSefFtwu+L126xDyGPjtnSo21ACEpKQlPnjyRKRtCwNVZzZQKEAICAphj/vrrL+YY1k4LhBSldOnSWl1fXrhwAQMGDECvXr309jmDmJ5//vmH+fliixYtuJ+pEOOjUqm41m7oWmxsLJYsWaLvNAghRaAChDfVVfN6rCiKsXJMLIriHQAP1byN7QmUbqk7duxbY2ouTM3rSj5uhBA9aNVKXc3Uu6gAwbTs27ePa+f8cePGwdLSUqu5bWxs0LKl9s17rKystFr8K4fFixfD2dmZOe7y5cvccx49ehR//vkndzwxPjExMfjzzz+xfPlybNu2jfkGnZR4do9XUgGCLvj5+WH+/Pk4c+YMkpKSkJOTgzt37sDNzY1rvOXLl0ucoWkaNWoUnj17xhwnRfeDl5o3b441a9Zwt1v/448/jGJXqi1btjDH9O/fHy1btsTYsWOxefNm5vioqCjUqVMHtWrVQunSpTFmzBg8f/6ceRxTk5ubiyNHjjDFeHp6cp/vDEm9evW4Cmf//vtvGbKR3q+//oratWtjxYoVuHv3LkRRxJMnTxAWFoapU6eiVq1aqFWrFndBESlYbm4u5s2bJ+mYXl5emDJliqRjvq1+/frMMXS/gsiJpwNCxYoVJc3B3NwctWvXZoqJjIxEamrqO38/aNAgxMbK8ohHI7paOC83JycnDBw4kDt+xowZyMrKQnh4OPMC8oYNG8LOzo57bhY8BQg8i8VN3cOHDzFo0CDmuJSUFPz0008yZMQmKysLISEhknW/2LNnzxv/feHCBeYxTLkDApDfBYEQubAWIFhZWaFcuXLyJKNArB0QgPwu2KyoAwKR2ogRI7QeY8OGDejduzdtMkF0grWQGwDee4/27iVvGjx4sM6KUgIDA7F+/Xqu2CVLltBmWIQoGBUgvKmmmtcjZJ5f3VZ4ityyQhAESwDqPuXJeezUHTd/QRAsZJyfEGJg/Pz8mHcsPH78OHJycmTKiCjN2rVrmWNcXFwwZMgQSebv2LGj1mM0btwY1tbWEmQjHQcHB0yePFnn83777bd0w4/g9OnTaNu2LXx9fdGnTx8MGTIEnTt3RsmSJTF+/HhkZ2frPCeeAoTGjRvLkInhMDMzg5eXFyIjI+Hj48Mcv2nTJiQlJcmQmek4cOAA96LfHj16SJrLBx98gD/++AOCIDDHxsbG4tChQ5Lmo2sJCQlcXT1e73wQEhKi1a5xjx49wo8//ojGjRvj3r173OOYgvDwcCQkJDDFmEL3g5dGjRrFHLNhwwbFP3jYsWMHhg4diszMzCLfd/78eYSEhOCbb76h61aJHDhwAPfv35d0zD/++AMODg6Sjvk2nsXJVIBA5HTz5k2m99va2sLDw0PyPOrVq8f0/ry8vHd2ET9x4gS2b98uZVrMmjVrptf5pTRx4kS4u/M17b537x5+++037N+/nzm2TZs2XHPyqFKlCvNnDZ7F4qZu0qRJSE5O5opduHAhd6xUfvrpJ0kXvJ86deqNwpzz588zj0EFCFSAQOTDWoBQsWJFk9ptmqcAgad7EBUgEKkFBwejVKlSWo+zceNGLFy4UIKMCCkaz7MNnk06iXHz8vJC7969ZZ9HpVJh0aJF6NatG1e3pHv37nF9LiKE6AYVILxJ3R0ZubcuuajmdUUWICC/+KCoBf65AKJknP8KgKKeOFsCoD58hJBXBEFgfuCXlpaGiAi569CIEqSlpWHbtm3McWPGjIGtra0kOXTo0EHrMZS6i8GAAQNgZWWl0zmvX7+Ow4cP63ROohypqakYNWoUGjRoUODihvT0dMyfPx+tW7dGenq6zvISRREnTpxgiilRogQqVaokU0aGxdnZmWvn9szMTK4iM5IvNzcXn3/+OVdsy5YtUbZsWYkzAvr164c5c+Zwxa5YsULibHRr+/btzF0cWrRo8U43oqFDh2qdy/nz52mHLzXeXgSpCdbdng1Z69atUbxhYpTzAAAgAElEQVR4caaYuLg4nD59WqaMtJeSksL88zVt2jSu32/kXf/73/8kHe/TTz9FixYtJB2zIB4eHszXexEREXpffEmM1507d5je7+vry1Ucqg5Pd5AePXq8UTg7c+ZMKVNiVqFCBZQuXVqvOUjJ09MTBw4cYN7k5aVJkyZxnavbtm3LNR8PW1tblC9fnimG7h+ziYuLw6pVq7jjk5KSsGjRIgkzYpOTk4Mff/xR0jHz8vJw4MCBV//NWtTi7OyMMmXKSJqTPlEBAlGSnJwc5u5QlStXlikbZXJ1dZW9k6Otra0s9xiJabOwsJDkHikAjB07Fr1798bDhw8lGY+Qt6WkpODMmTNMMaVLl5a8WyExDl999ZUs93Fe9/PPP6Nx48YwMzPj/vzG83yFEKIbVIDwJnW/bdn7DbOJUfN6BZnn56XuuN0VRTFLrsn/HVtd32SlHjtCiJ7w7CoYGhoqfSJEcbZt24aMjAymGAcHB0nac75Urlw5rXYjBvIXcSlR8eLFJSmwYPXzzz/rfE6if7dv30bNmjWxaNEitYtiQ0NDMWPGDB1lBsTExODx48dMMQ0bNoRKRR/hXgoICODamWPOnDm0SI/TH3/8gcuXL3PFTpo0SeJs/jN+/HgEBQUxx/3999948uSJDBnJLysri6vwIiQk5J2/a9++vRQp4cSJE9i6daskYxmjPXv2MMcotaBUDlZWVggODmaO4+3Iogu///47Hjx4wBw3ZswYvHjxQoaMTEd8fDz++usvycarUqUKd7EbD9b7FXl5eTh+/Lg8yRCTx1qAwNOlTBOsHRBe6tmzJ/7++28kJSXh4MGDEmfFpmnTpnqdXw7+/v7cC+6Tk5MRE6PukdibnJ2dUaOGbvfpYt1J/s6dO2/sXk+KtmjRIq27Di9YsID5Xq5UduzYgbi4OMnHffnZITs7m3ln8MDAQNkXEOmSq6srnJycmGKoAIHI5fbt28xddE2tAAHg64LAomrVqnSPnMhiyJAhsLAoat9Vza1duxalS5dG06ZNsXr1aq2vdwh5XWhoKPPGRK1atTKqa0QincqVK6Nbt26yjT9r1ix88sknr/67SZMm+PDDD5nHoQIEQpSLrszf5KXmdbZ+w+zUje8t8/y81OUl93HTZA6lHjtCiJ7wFCAcPXpU8jyI8vz555/MMYMGDYKjo6OkeXTs2JE7Vh8PZFn06tVL53Nu27ZNlgeCRLlu3bqFRo0a4eZNzS9Fv//+e+YFEDxEUWTuxAMAjRs3liEbw8ZT/BUbG4uePXvKkI1xS01N5S4i6N27t6y7RguCwL3Ttb+/v0Eu9P35558RHR3NHNe5c+d3/s7X1xe+vr5SpIUffvhBknGMTUJCAvNO/c7OzibVAQHI36ma1Z9//onMzEwZstFOXl4edwFsXFwcNmzYIHFGpkEURaxevRolS5aUbMwyZcpg7969sLGxkWxMdXjuVxw5ckT6RIjJS01NRUJCAlOMt7c8t+FLly7NvaP3Rx99hKVLl+p90U/Dhg31Or9cPD09mT53a6N169Y6X3DIc3+Ndcd6U5WSkoKlS5dqPc6zZ8+wfv16CTJiJ9eGJ3v37kVeXh6uXr2KrCy2/d1Yi2aUThAE5i4IVIBA5HLt2jXmGCpAkJ6fn5+s4xPT5e7uju7du0s2niiKOH78OPr164eAgADcvn1bsrGJaTt06BBzjCltdEPYTZw4UfIxnZ2dsXv3bnz11VfvvDZv3jzY2toyjXfq1CkkJSVJlR4hRELm+k5AKQRBcAdgreZt7NuWsbmv5nUbQRBcRVFU2haN5dS8LvdxA9QfOypAICYrOjoa169f13cazJydnSV9MJeQkICwsLBX/52Xlwd7e3ukpqZqPMaRI0ewdetWmJmZSZaXJnh29C3Kjh07JB1PVxo2bAhnZ2fJxgsLC3vnQXpycjL27dvHPFbZsmUlP67a/FtbtWql8QNZfZwjBEGAtbW1Tncny83NxW+//Ybp06cX+Prb5whDQueIfG+fIyZNmsTc4jYnJwfLli3DnDlzCjxHSEEURe4dkQH1X59KlSpJ2kZV6dcRoijC09MTsbHqGqK9ad++fWjRogU2b96s0U56dI7IvyHI2rUDyO8S1L59e52cWzp06IDdu3czxTx9+hRdunTB9u3bma7x5DpHaCIlJYWrGMTX17fQ373t2rXD4sWLtU0NJ0+exKpVq5h3qNQ1uT9rvC47OxvTpk1T24nnbX5+fszfzzyUdB2RnZ0NW1tbpKenaxzz9OlTfPHFF1p3/5L6s8ZPP/2EGzf4G6nOmDEDxYsXlywfTRnydURmZiYWL16MY8eOSTKeIAj46KOPMGvWLLi6umo1Fut1BOtCQyC/4JqncEEdJZ0j9EkX9yOUiLX7AZDf0VFTrOeIsmXLcm0skJ6eXuADb11r0KBBgX9vDJ81ypcvj+HDh8vefdLV1VXn5xGec3JERIRWBdiGco54G+t1xPLly5GSkiLJ3N9++y2cnZ11uqtrXFycbJ1VHj16hIiICERFRTHHiqKomN+3Ul1HVK5cmek8ef/+ffz11186LSAtiqleR7zNkD9rvLR582bmmCdPnrzxM6nL+xH6wrorN6vCChCUcu5jReeIfEo5R9SuXRtr166VLI+XoqKiULt2bSxYsKDI+6amcI7QFN2PyFfQOYJnE4pWrVoBoHPES0p/9lkYOc8RdevWxZkzZyQZt27duvjkk0+Qk5NT6M9ejRo1cOLECY3HzM3NxZw5c9CoUSMAyj1HSP29SoghoAKE/5TS4D2PZM5Bk/FLAVBaAYK6Yyf3cdNkDk2+voQYpdTUVOYFmMboxYsX7xwHX19fnD9/XuMxMjIycPr0adl2c9MVQ/1+kHp34oSEhHeOxeHDh5GXl8c0jru7O6ytrSU/ro6OjlyLagGgffv2Gr9XX+eIgIAA/PPPPzqdc9myZZg0aRIsLS3fea2gc4SpMtTj8Po5Ijo6mnv3u//973+YMWNGgecIKezfvx/btm1jjjM3N4ednZ3anEqVkvay1xCuI+rVq8d1rjx69CiqV6+O7du3q93V0tTPEdnZ2fjtt9+4Yrt374709HSmBcW8atSowbVge/fu3fj2228xdepUjWPkOkdoYt26dVzH08/Pr9Di2/bt20tSgCCKIvbv34+mTZtqPZYhKewcERcXhxUrVnAVnfn4+BjkeUfbnKtXr45Tp04xxWzevBl+fn5aLTiT+rOGtrvwRkdHIywsTOefPQ31OuLp06dYunSpJB3PHB0d0aZNG8ydOxdeXuqa5WqG5zrC3d0djx5pfkv11q1biImJUcxCu8IY4nkN0M39CCXi2eWX5bzFeo5wd3dnzkcpHB0dUalSpQJfM5bPGhMnTsSKFStk7S5WqlQpnR8rOzs75hiW+80FMZRzxNtYriOys7OxcOFCyea+desWTp48ifLly0s2pjobN26Udfy5c+dydX6xt7c3yO+forB2QACAixcvMhXFyclUryPeZqifNV7HU2SuUqlkzVOJ1xE8vztZFFaAoLTjoCk6R+RTyjnC1tYWjRo1wsmTJyXNB8jv2rRkyRIMGjRI8rELo8RzhL4Y6nF4+xzx7NkzXL58mWmMypUrv/oZo3NEPkN49qkLr58junTpgsjISO5nedbW1qhRowaaNGkCHx8f5OTkFHmMfXx8mAoQAODEiRPw8fHhyk8dqb4fpP5eJcQQ6LZXqbKpKytOEUVRvjunAERRzACQpuZt0pU/S0ddTroomFA3hxKPGyFEz3gqT3kqyolhyMnJ4dq1qm7durLsriUIArp27crcWr5kyZLo2bOn5PlITcrqfE09evTIYHe4IGw2bdrEvNP0S48fP8bOnTslzihfeHg4Nm3axBXr5eUFCwsLiTMyDvXr1+fuThQXF4dmzZrh7NmzEmdlXI4cOcLV/cDPzw916tSRIaOClS1bFp6enlyx06dP13qhkC6kpKQw35QF8q8rivpaNG/eHFZWVtqk9oohHEe5iaKIAwcOYPbs2dwdbwp7oG7satWqxRxz//59rkWycrl3754kv1eOHj2qfTImID4+HvPnz9e6+GDw4MGYOnUqZs+ejT59+khWfMCL9X6FKIqIjo6WKRtiqnh2RZRzsadcD5kLYm5uDhcXF8nGq1evHvP9HUNTunRpDB8+XLbxPTw89NJly8HBgbkrEV0Pq7dhwwaujQSKostrp8zMTOaiWVYbNmzAihUrmGLMzc3h5uYmU0b6w1OAQAvKiBxYCoQBwMnJCcWKFZMpG+Vyd3eX9brHVO+XEN3p1asX1+8eTZw5c4b5XELI606ePMn8/PNl9wNCiuLk5ISPPvqI6drF0dERjRs3xvDhwzFv3jz0799f43s3PL/PIyMjuZ//E0LkY9x3PNmUUPO6NH1A1VM3j7o89UEJx86gjpsgCCMEQYiU+w8A3W33QogB4ilAOH36NO7fvy9DNkTf/vnnH66H63Xr1pUhm3xVqlRBv379mBbWTp06FdbW1rLlJJVKlSohICBA5/OuWrVK53MS3du1a5dW8cuXL5cok//ExMTg999/547X5S5+hsbe3l6rRe6pqalo27Yt864xpoSnTbNKpUL37t1lKdIrCm+BW15eHr799luJs5HeqVOnkJOTwxxXs2ZNlCxZstDXbWxs0KxZM21Se+XatWs66XihVHl5eVi/fj02btzI9bUC8otpHBwcJM7MMFSpUoVrt8RDhw7JkA07URQxZswY5q5qBTl37hxSUnR1O9Iw5eXlYcmSJUhKStJqnG7duqFOnTooVaqUYhYI89yviIqKkiETYsp47pHIWbzj5eWls/sdbdu2RcuWLSUbr0GDBpKNpWRTpkyRrVNF1apVZRlXE6xFzteuXcPz589lysbwiaKI+fPnSz5ueHg4kpOTJR+3IKGhocjIyJB9HtZrwdKlS3Nv0KBkPItAaXEnkZooilxdzUyRhYWFbP92Ozs77s1HCNGUhYUFRowYgZo1a8oyPm12SLQRGhrKHNOiRQsZMiHGyN/fH1OnTkXNmjVhaWlZ4HsEQUCVKlUwcuRIzJ49G3379kVAQADzJnqOjo7MHd+SkpK4N3wihMhHGU9UlEHd1ilUgFA4JRw7QztuJQFU1cEfabawJMRIlSpVCra2tsxx06dPl2RBCVGOnJwc7N69mznO29u7yMV8UmjQoAGmTJmCatWqqV3EOWXKFIwYMULWfKTUvXt3ne8AtHv3bjx9+lSncxLdSkhI0HoXut27d6NHjx5YtmwZwsPDtd5NISMjAytWrOBeiApAtpvdxiIoKEirDhGJiYlo3bo1bt68KWFWxoNnJ++mTZvCw8NDhmyKVrduXZibm3PFbt68GZGRkRJnJB1RFLnaf5ubmyMkJETt+zp16sST1jvy8vJMuqAnNDRU691XTXk3P3NzczRp0oQ57vLly3pfZJSamoo6depgy5YtkoyXk5PD9TNvSsLDw7V+6KRSqWQtKudVqVIl5hgl/w4jhom1AMHZ2Rn29vYyZQNYWlqiUaNGso3/usDAQDRs2FCyexamUoDg6OiIxYsXyzK2IRUg5OXl4dKlSzJlY/iOHDkiS5eI3NxcnVw7ZWVl4cCBA7LPw8NYF+V6eXkxn4+pAwKRWlpaGnNxmakWIADynY/8/Px0vtkJMU0WFhYYMmQI1z0qdU6dOkXFqoTbsWPHmGOaNm0qQybEWJUoUQKffPIJFixYgAkTJmDYsGEYMGAA+vTpg48//hhz587FmDFjUK1aNa03cuF5DkIdBwlRHipA+I+6Oxe6ugJUt02gEvv0KeHYGeJxI4TomUql4nqoDwDDhg3DsGHDMHr0aEyYMAErV67ErVu3JM6Q6EpYWJjiuh+8zt3dHSNHjsSMGTPQt2/fN3bDFAQBzZs3x+HDhzFt2jSd5COVkiVLYtiwYTrdmSsnJwfr1q3T2XxE9/bu3StJkVhmZibOnTuH3377DYsXL9ZqF70TJ05wnWNe8vb2Rrly5bjjTYGLiwu6du2q1RiPHz9Gp06daLfpt4iiiDNnzjDHBQUFyZCNenZ2dlotTJs9e7aE2UgrJiYGjx8/Zo5r1aqVRgWTffv2hY2NDU9q74iIiJBkHEOTkZGBHTt2aD2Ov7+/BNkYrubNm3NdHx4+fFiGbNQTRRFr1qyBh4cHwsPDJR373Llzko5nbI4fP671GH5+fihevLgE2UjLwcGBuZAvPj4eT548kSkjYori4+OZ3q+LzywtWrSQvQuCk5MTPD09YW1tLdlCDSUWOsmlS5cuCA4OlnRMc3Nzrs4wUilbtixzDC2KKJwc3Q9e0sVxP3HiBFJTU5njevToIUM2bzLWAgQzMzPm5zj6Lk4mxofne8qUCxB8fHxkGZe38ykhPFQqFfr06YPhw4fD2dlZsnFfvHhBG04QLqmpqcz33atWrSr7ZorEOJmbm8Pb2xuBgYFo0KABmjRpglq1aknauZmnAOHEiRPIzc2VLAdCiPaoAOE/BfeO+Q//dqVs1M2jLk99UMKxM8TjRghRgHr16nHH5uXlITMzE4mJiQgLC8PcuXOxbds26o5gYB49eoRNmzYxx5mbm6N27doyZFS4kiVLYvTo0bh+/Tri4+MRExODzMxMHDlyBC1atDDInV8qV66Mfv36ccW6u7tzPcRfuXIl13zEMOzatUvyMa9cuYLly5dzn99Pnz7NPbeNjQ0GDx5skD/futasWTNUqVJFqzGuX7+OKlWq0M2r19y9e5d58Vm1atVgZ2cnU0bqvf/++3BxceGKXbduHWJiYiTOSBonTpxgjrG3t0f79u01eq+joyPmzZvHPEdBoqKikJGRIclYhuTs2bNIT1e3P0LRrK2t4e3tLVFGhsnR0ZHrOvvUqVNaH38WqampOHjwIJycnNC3b19Z5o6Li0NSUpLk4xqDtLQ03LhxQ+tx5NjNUCo8u31TFwQiJdYial0UILi4uGDs2LFcHU01Vb169Vefv1q3bg1LS+0eLVStWhWOjo5SpGYQBEHAkiVLJF2U4Ovrq/XXQRs8BQimWpCrzpUrV7Bnzx7Zxo+NjZX1ejAnJwf79+9njvP09MTq1atRrVo1GbL6D8/3qqFgvd/z9OlTrTqREvI2ngIEfXQGVYry5cvLMm7r1q1lGZeQogQEBGDatGn44osvuJ9nvu3IkSO0noAwCwsLY352Rd0PiJKVL1+eudNZYmKiSXfgJkSJqADhP0pYRK/JPEpcSK+EY2eIx40QogDVqlWDm5ubJGOJoojdu3djzZo1dNPAQGRmZmLp0qXIzMxkjm3UqJGkD1NZOTs7w8fHR68PYKVSv359fPDBBxovsHZwcECPHj0QHR2N//3vf8zzRURE4MqVK8xxRPlycnJke5AdHR2Nffv2McelpqYiNjaWa05ra2uMGjWKeyG1qVGpVOjfv7/WO7g/ePAA5ubmWLZsGRUiAFzdDypXrixDJpqzs7PDp59+yrXQKy8vD3PnzpUhK+1kZGRw7azesWNHpl2Chw0bhhEjRjDP87bs7GyugglDJ8XuZQ0aNNBpdyilatWqFXNMVlaWJDviq3Px4kW0atUKDg4OaN26tVZdkjQRFRUl6/iG6sKFC1p/7q5UqRKqV68uUUbS4ylAoO8XIpWMjAw8f87W2FhXBXRly5bFhAkT0KxZM1nGDwwMfPX/HRwc0Lx5c63Ga9CggZYZGZ7SpUvju+++k2w8nvOhlJydnZk/Z1IHhIL98MMPso4viqIkBYqFOX36NBITE5njhg4dCktLS8ycOVOGrPLZ2NjAy8tLtvH1jbUAIS8vjzpDEUlRBwQ2pUqVgpWVlaRjWlpa0kJaojeWlpYoX748GjVqhF9++QXt27fX6v5dQkICLl68KGGGxBSEhoYyx8j1uZkQKZibm3M9Tzx69Kj0yRBCuFEBwn/UHQtdrTxRN48Sn0Ir4dgZ4nEjhCiAmZkZ3n//fUnHPHnyJNavXw9RFCUdl0jvzz//xMOHD5njzM3N0a5dOxkyMl0tWrTAmDFj4OTkVODrDg4OqFu3LkaOHIk5c+agT58+sLe3R5UqVVCnTh3m+VatWqVtykSBTp06JesuwXv37mXe0fv69etcc9nY2GDMmDEmvws2KycnJwwaNAgWFhZaj/Xxxx+jYcOGsi8sVbqzZ88yxyjh+9bd3R3jx4/nil27di3zgju5nT17FllZWUwxlpaWqF+/PlOMIAhYtGgRhg0bxhRXkEOHDhl9EU9mZia2b9+OuXPnYvbs2bhz545W41lYWNBufv/y8vKCr68vc9yRI0dk/b5bv349atasicOHD8s2x9toR/uCabuw09nZGX369FF0l6mKFSvC3NycKSYqKop2+yWSYO1+AOimA8JLrq6u+PDDD7mv9wpTrFgxVKhQ4Y2/07YLgikWIAD5n6caN24syVj+/v6SjMNLEASUKVOGKeby5ct48eKFTBkZpsTERKxbt072eXjvw2iCp9jVwcEBw4cPBwB06tRJtnOCv7+/URcy83S85LnvT0hhWAsQihUrptcNrPTNzMxM8mvDhg0bytoFixBNqVQqdO7cGdOmTUPz5s25n0WcOnVK4syIsePpAE+FW0TpXt8EQlNXr16V9XMfIYQNFSD8R92TGbanPfzUzZOtkyzYKOHYGdpxewogSgd/6A43IRqoWbMm80MkdUJDQ7F161ZJxyTSio6O5trVGcjvflCiRAmJMyKVK1fGrFmzMGHCBHTr1g0ffPABBg0ahBkzZmDu3LkYNGgQqlWr9s7DNJ6Wp1QkZJx4br6xyMzMxLFjx5hirl27xjXX6NGjdbqAx5j4+/tj8uTJWndCAPJ3/w8ODjbphXysvytVKhU8PT1lyoaNi4sLBg4cyByXlpYmyU72UuLpflCrVi2m7gcvCYKAxYsXv1okwysxMRHnzp3TagylysvLw7Jly+Dj44Pg4GD8+OOPWhcfmJmZYejQoXSN+Zr33nuPOSYpKYnr50UTYWFh6N+/v8673UVFRVGHvbc8f/4cV69e5YoVBAHVqlXDhAkT4OrqKnFm0rK0tHxnIbQ6OTk5OH36tEwZEVOi9AKEl3x9fSVdzFujRo13FhBp2wWhYcOGWmZlmFQqFZYtW6Z1505PT0+UKlVKoqy0y4NFVlYWdUF4y59//snVhZaVXAtRkpKScPv2bea4ESNGvOrOJwgCZs2aJXVqAICAgABZxlUKnp1ReXasJ6Qgubm5iImJYYpxd3dXdLGzLpQvX17S8WjDBqI0rq6u6NWrF+bNm8fVxToyMlIn10bEOOzcuROXLl1iivH19VXEZylCisL7HGvp0qUyZEMI4UEFCP9Rt5WgrgoQ1JXHsm15qBtKOHYGddxEUVwiiqKf3H8AsN0NIcREvdypQGp79+7FlStXJB+XSIN3xy3qfiAvlUoFb29vtG7dGi1atEDdunXh6upa5M36Dz74gHmHkdjYWKqMN0J79uyRfY6DBw8iO1vz2laeAoT+/ftT8YGWAgMDceHCBa0WCr0UGhqKvXv3ap+UAcrNzWVeyFu6dGmtFxlJqW7dulw32XW5u7k6ubm5uHnzJnOcNjvOqlQqLFmyBGvXrn21WIbHgQMHjK7gLzo6Gi1btsTHH38s2Y6a3t7e+Pzzz/W+u6/SBAQEcD3A3bdvn+QL9l+8eIGBAwcydyKRwvPnz7UucDE2ly5d4up00b59e8yaNQsjR440mN1I/fz8mGNWrVrFfM1KyNvi4+OZY/T1GaZz586wsrLSehwzMzN06tSpwNfatGnDteNuzZo1uXbtNhaVK1fG119/rdUYLVu2VMQCzrJlyzLH0K62/xFFEcuWLWOOq1ixIvPnufv37yM1NZV5LnVYF3wBgLW1NT777LM3/q558+aSL6I1MzPjumYwJBUrVmQ+F1ABApHK7du3mbviuru7y5SN4aACBGIqrK2t8fXXXzN/JsnJyaGul6RIubm5WLlyJWrWrImgoCDmeOp+QAyBpaUl18YNK1eu1Mu9ekLIu6gA4T/qnsjoagWFQS2k/5cSjp0hHjdCiIL4+/tLfjMMANauXWvSuyYr1bNnz7h3IaPuB8rj4uKCjh07MscdPHhQhmyIviQmJnI9DGaVkpKi8SKC+Ph4PH36lGl8CwsLk90hU2oVKlTA4cOH8fnnn2s91v/+9z/tEzJAV69eRXp6OlOMl5eXTNnwUalUaN++PXOckgoQnj59ynw96ebmJsm1ba9evXDr1i3uzl6xsbHcnWCUaM+ePQgMDERoaKgk4/n7+2Ps2LH48ssvZfksYuhUKhVatmzJHBcXFyf5jsNz585FdHS0pGOyoAfSb4qIiGCOCQ4ORufOnQ3us1zVqlW54jZs2IBvvvmGu1MEITwdEPR1Hejo6Ii2bdtqPU6nTp0KLXyzt7dHnz59mMd8e+GxKRo3bhzc3Ny4Yu3s7FCnTh2JM+Lj7e3NHEMFCP8JDw/numfTpk0bVKxYkTlOjus2nvw/+eQTlCxZ8p2///7779/p8KqNihUrcu0aakisrKwKPJZFoQIEIpWoqCjmGKm7rhsint+dhXFyckLNmjUlG48QqdnY2HB1ZqOOWaQwFy5cwDfffIOJEydyf580a9ZM4qwIkQdPsUxiYiIOHTokQzaEEFa62tXfEKSped1eJ1kA6rbfUpenPqQBKGpLRF0cO0M8boTohL29PTw8PPSdBjNnZ2dJx7OyslJ7HEaPHo2xY8dKWjCQkJCAyMhIRe2Yb4jfDwAk2c3upd27d3Ptimpvb49BgwZptROwtuztpf21aizniP79+zMvjjx48CBGjhwJQLNzhKkw1ONw4cIFnc116NAhdOvWTe3D4suXLzOP3alTJ62+BnSOyPfyHCEIAubNm4f09HSt2nGeOXMGo0ePho2NjVQpGoSzZ88yxwQGBirue6ZTp07Yu3cv7t+/r3HMuXPnkJycjOLFixf4utTXqkW5ffs2c0y7duoB/s4AACAASURBVO0K3CmU5xzh5OSE4OBgJCYmwtPTE2lpbB+vL126xLWIXE48X7/du3cjODhYss8KTk5OmDVrliRjKYnUP/9dunTBzp078fz5c6a4PXv2oH379hov7Crqs8aNGzcwc+ZMpvmlFh0drZNzq5KvI0RRxLlz53D48GFcvHiROb5Dhw4a56KP+xGFcXd3h7OzM9di8KdPn2LhwoUYN26cYna9U9o1gqakvB8B6PY6ghdrEaqLiwvs7OyYYqQ8R/Tt2xdhYWFcnRvMzMwwbNgwtGnTpsj3dezYESVKlMCPP/6o0e+lkSNHonfv3mrfZ+z3I2xsbDBhwgSuYowOHTpwdR6Qg7u7O+zt7Zl21g8LC+OayxDOEQUp6jpi+fLlzOP5+Pjgvffeg729PY4ePcoUGxsbW2hHEx6ZmZnMnVRVKlWhGyJUq1YNn332GebPny9FemjSpIlRn0deKleuHJ48eaLx+58+fQp3d3e9d1ExxeuIgij5s4Y6N27cYI5p3Lhxgfkp6bOGLnh6eiI2NlbrcVq2bKn2872Sj0NR6ByRz5DPES91796d+ZrlypUrcHFxedXl3dTOEbpkKMchKysLP/30E44dO6b1WAXdC6JzxH/jGcr3xOuM9Rzh4eGBgIAA5nu+GzZs4NqArLAcpCD19yohBkEURfojigAQAkAs4s8jHeXxRE0ewfo+VgXkfFFNzkN1kMNwNTmc1/dx0tPXJrKg41G1alWREFKw7du3i8WKFSvqfML8x9PTU8zMzNT3P428pmvXrlxfy61bt+o7dVKIzMxM0cbGhunrWbx4cTE7O1vfqRuN9PR0MSMjQ2/zjxs3TtJztxTng169ejGPu3fvXh0cLdOTm5srdujQQauv+Y4dO/T9z9C5oUOHMh+nCxcu6DvtAg0aNMhgv+bffPMNc+7nz5+XJZfPPvuMORc3NzdZctGl7OxssUKFCjr/PULyjR07lusYr127lmu+Fy9eiFFRUeL9+/fFvLw8sXXr1pJ93QVBEFetWiW6uLgwx8XHx0t8ZA3HrVu3tPo6BAYG6vufoJWBAwdq9X1XrFgxxf5+JsoVGBjI9H1Wp04dfacsrl69mvnno02bNmJkZCTTPM+ePRPnzJkj1qxZs8AxHRwcxO+//17MycmR6V9qeJ4/fy56eHgwfW3MzMzEuLg4faf+ho4dOzJ/j927d0/faetdamqqaG9vz3zsFixYIIqiKCYkJIiCIDDFVq5cWdJ/w9atW5nzb9KkidrjUqZMGa1+x7/8c+fOHUn/vUrFc+/v4cOH+k6bGDiec5Cbm5uYm5ur79QVYfDgwZKc55YuXarvfwohGqlatSrz9/euXbv0nTZRiLy8PLFbt26SnDf9/Pz0/c8hhMnmzZuZv8+dnJzErKwsfadOTFgRv/cjRQWsG9bVHxXIS+q2kdLVVscFb6/4H/btruSnhGOnbg4lHjdCiAIFBQUhLCyMq7VzYWJjY/H7779LNh7RTmZmJvbu3cscN378eAQHB8uQEZGClZUV866eycnJCA8Plykj05Cbm4sFCxagfv36KF68OKytreHh4YGPP/5Ykp2NWBw/flyn861Zs6bI10VRxOHDh5nGtLCwQOPGjbVJixRCpVJh9erVWrX+Zv16GgPWDgjW1tbw8/OTKRvt8OzAr5SveWRkJNP7VSoVKleuLEsuo0eP1nhH+ZceP36Mx48fy5KPrmzcuJFrx8PCuLq6okOHDpKNZ+w+/fRTqFTstzCnTZvG1LEiIiIC7dq1g62tLapWrYrSpUtDpVLhwIEDzHMXpESJEli/fj369u2rdpftt4miaLItpZOTk9G2bVutvg5du3aVMCPd03Ynr8zMTAwfPvzlZiVEgW7fvo3NmzdjwYIF2LVrFx4+fKjvlHDnzh2m95crV06WPFh8+OGHTPcFpk+fjr1796Jq1apM8zg5OeHLL79EeHg4YmNjsWbNGkyfPh1fffUVNmzYgJiYGIwdO5b5msmYWVtb46uvvmKK6datG0qXLi1TRnwaNmzIHHPq1CkZMjEsGzZsYOocAQCWlpbo06cPgPxrqICAAKb4a9eu4cGDB0wxRdm+fTtzzPvvv1/k63Z2dli4cCFvSq8EBgbCy8tL63EMAc8zGyk/xxHTdPDgQebr6DZt2nB9hjVGDRo0kGQc1s/QhOhLSEgIc8yWLVtkyIQYooULF2Ljxo2SjDVq1ChJxiFEV4KCggrs6l2UxMREk71nToiS0Cef/6jrzWslCIKsC+kFQXAGYKnmbUpcSK/u2LnrIAd1vXCUeNwIIQpVo0YNhIeH46OPPpJszJkzZyIjI0Oy8Qi/Q4cOIT09nSlGpVJh9uzZMmVEpNKqVSvmGPpQyu/u3bto2rQpxo4di9OnT79a4Pfo0SMsW7YMvr6+OrtxmpaWpvNikp07dyIlJaXQ1yMjI5kX3DZo0AC2trbapkYKUaJECWzevBnFihXjilfKYnRdEUUR169fZ4qpWbMmzM3NZcpIOy1atGCOUcrXnLUAoXz58tzf5+p4eXmhR48ezHGXLl2SIRvdkerBz0t9+/Z91VqdqFeuXDl069aNOS46Ohp//fWX2vfl5OTgyy+/RK1atbBv3z6mogVN2Nvb46uvvsL169df/fzwLCjXdbGlUgwZMkTrhWOGXoAQFBSkdYv1sLAwtQW0RLcSExOxaNEi1KhRAz4+PujatSvGjh2LTp06wdPTE5999hmSk5P1kltSUhKSkpKYYpRQgKBSqbBhwwaNFuLOnz8fkydPhiAIWs1ZpkwZ9O7dG5MnT8asWbPQrVs3uLi4aDWmsRoyZAh8fX01eq+lpSWmTJkic0bseBZRUgECsHz5cuaYLl26vPGzxPN57u+//2aOKUheXh527tzJHBcUFKT2PSEhIfDx8eFJ65UuXbpoFW9IKlSowBxDBQhEW/v27WOOadu2rQyZGKZGjRppPcZ7772n1aYyhOgSz+/lrVu3IjMzU4ZsiCE5dOgQxo4dK8lYPj4+6Nu3ryRjEaIr5ubmXM+epH52QwhhRwUI/7mnwXvcZM5Bk/E1yVPX1OUk93HTZI67OsiBEGJE7OzssGLFCly4cAE9evSAlZWVVuPdv38f3bt3px0HFWDr1q3MMZMnT6ad6wzAe++9xxxz8OBBGTIxfrdu3ULTpk0RFhZW6HuysrLQq1cvnSyW++effyRfLKhOZmZmkTvghYaGMo/JU0RD2AQGBmLTpk1cv9cvXryI+Hh1tdfGIyEhAWlpaUwxderUkSkb7Xl4eDB3BVDC1zw7OxvR0dFMMXJ3oeAp0r18+bIMmehGVlYW14KDogwcOFDS8UzBlClTuBaJzp49G3l5eYW+/vjxY7Rp0wZz587VJr1C1alTB9euXcOsWbPeWETHs3ujKRYgnDhxAhs2bNBqDH9/f1SpUkWijPSjWLFi+Oabb7QeZ/z48UUW0BLdyM3NxQ8//ABvb2+MGjUKFy5cKPA9CxcuRMOGDZl3DZfCzZs3mWOUUIAA5HcZOnjwINq1a1fg615eXjhw4AA+//xzHWdGihUrhk2bNsHa2lrte2fMmMHcmUIX6tSpw7yjtakXIERFRRV576gwgwcPfuO/eQoQli9fLsm9+J07d+LJkydMMRUrVkSlSpXUvk+lUmHQoEG8qcHS0lLSTZSUjgoQiK6Josh1P6B169YyZGOYKlasqPV9KtYuSoToU40aNZg7E8XHx8Pa2hoLFiygz+wmSBRFLF++nOsZd2FWrFih0ecuQpSGZxOiLVu2IDs7W4ZsCCGaogKEf4mimAb1u+TL3cNS3fhPRFFk27JZN+6oeV0XvT/VzXFbBzkQQoxQQEAA/vrrLzx79gx79uzBxIkTuW50A8CuXbugUqkwduxY6oagJ7m5uVwtsw19x0xTUb16deadBk+ePInnz5/LlJFxunnzJpo1a4Z799TXxb548QJ9+vRh7jrCSl8L8tatW1foa//88w/zeFSAoBsdOnTgKhABgKNHj0qbjILduXOHOUaJi4Re17JlS+YYfX/Nb9y4wXzzVO4ChMDAQOYYQ+6AcOzYMeZinKJ88MEHsn+NjJGfnx8++OAD5rjIyEjs2LGjwNcePnyIhg0b4siRI9qmV6B+/fohNDS0wNbRrq6uqFGjBtN4ly5dYt6R3JCJoogJEyZoPc7IkSMlyEb/hg8fjn79+mk1xuPHjzF16lSJMiI8RFHEJ598gs8//1yj7gZRUVEYMGCAzjez4Okup5QCBADw9fXFnj17cPToUcycORMDBgzAuHHjsGnTJly5ckXShR2ETfXq1bFp0yaUKVOmwNctLS0xd+5cxRaI2NnZoXr16kwxERERJr2j7YoVK5hjvL293yk4aNq0KXPxR2RkJM6cOcM8/+uys7PxxRdfMMe9//77Gr+3f//+zP+2lz7++GOULl2aK9YQlSpVCjY2NkwxrAX9hLzu6tWruH//PlNMjRo14OrqKlNGhkcQBK0+13Xv3p3rfh4h+iIIAkJCQrhix44dC29vb5MvYDUlWVlZ6NevH4YMGSLZmMOHD0fz5s0lG48QXWrQoAHz55tnz57Jdn+fEKIZKkB4k7pF6nwrTjWnbnylLqLX93HTZA6lHjtCiIGwsbFBu3btMHPmTERHR2PYsGHcYy1YsAC1atXC7dt0atK1M2fOMO9Y5ePjA39/f5kyIlJSqVTMN6OzsrJw8uRJmTIyPk+ePEHLli0RFxenccy9e/e02s1NE8eOHZN1/MLs378fCQkF1zCz3iS2s7ND3bp1pUiLaKBevXpcOyofPnxYhmyUiec6RUkLzwrC88BS31/zK/9n777ja7y/OIB/npshIREiVhJNJIg9Y9WIvfesXdWp+KGoahWt0lqlRlujqpSatVJ7JGITQiJqRMUWETNBxvP7Q6mR5N7zvc/3zvN+vfr6/eo+5/s9bcm993m+55zoaHKM7MPtXl5eKFy4MCnGmicghIaGarZW4cKFpXXatwdjxowROpw1YcKE1w7vPnz4EM2aNUNcXJxW6T1Xp04d7Nq1C4sWLcq221jdunVJ66qqKtRB2Fr99ddfRn9ODwgIsJmJI4qiYMGCBUKFOC+aMWMGf/8xo7lz55IP465Zs0bT9yJDHD58mBwTGBgoIRPjhISEYNSoUVi4cCEmT56MDh06wM3Nzdxp2b3mzZvj9OnTmDhxItq2bYugoCDUrVsXI0eORGRkJIYPH27RU0jffPNN0vWpqalCRT224PHjx1i0aBE5rl+/fq995vPw8ED16tXJa82fP58c86Kff/4Zf//9NzmudevWBl/r4+OD5s2bk/dwcXHBqFGjyHHWTFEUcnMonoDAjCFyz7lp06YSMrFuPXr0QJcuXchxOXPmxJQpUyRkxJhcogUIwNODtA0bNhRuoMSsh6qq6NKlC5YsWaLZmn5+fvjuu+80W48xU9PpdEKNQdetWychG8aYobgA4WUxel7XPy/TOCX0vK4vP3PRl5eXoiiesjZXFMULQF49l1nqvzvGmJX6/PPPkSNHDuH42NhY9O7dGxkZGRpmxfQRuWHctm1bKIoiIRsmg0gnw61bt0rIxDb169cPly5dIsctX74c+fPnl9JJ/PHjx+RpA/7+/ujevbvRe6elpWHVqlWv/XpCQgLOnz9PWqtmzZpwcnIyOidmuFc7Khpix44dEjKxTCITEIoWLap9IhoS6fwjcgBPSzEx9K+ypiicpHZ+PXXqFNLS0iRlI1dmP+dFlClTBvv27UORIkU0Wc8eBQUFoVevXuS4Q4cOvfbz+4svvpAymWPr1q0IDw836OdNnTp1yOtHREQIZGV9MjIy8Nlnnxm9ztdffw1nZ2cNMrIMjo6OWLx4MTp37iy8hqqq6NOnj6aTXZhhUlNTMXHiRKHYkSNHIj09XeOMskb9/OPu7i48LZTZp1y5cmHkyJFYu3YtTp8+jbCwMEycONEqpkTVrFmTHGOvXWzXrVuXZdOGrOh0uiyLB0Xu4yxbtgz3798nxwFAUlISxo4dS47z9PQkF6q8++675H369+9PLgy3BdT3m3PnzvEzGCZMpHCXCxBepygKfv31V3Ih2ahRo/DGG29IyooxeWrVqoX8+fMLx6ekpKBTp04GTSFn1mvTpk2aHprOmTMnVq5cyUX3zOp16tSJHGNPTXsYs0RcgPCySD2v02aj01XW8/oxyfsLUVX1HwC39Vwm89+dvn9viaqq0k/KMcZYNnx8fPDhhx8atUZERAR+/vlnjTJihhA5xNeuXTsJmTBZRAoQNm3aJCET00hLS8OBAwewePFizJgxA1u2bMGdO3ek7BUVFYWNGzcKx9+6dQv169fH2LFjX+tCbIyjR4/i0aNHpJi6detiwYIFQjcxXrVs2bLXfo1aEAGIHWJgxsmXLx8qVqxIijlz5gxpAog1o05AUBTF4g9Wi/w3P3nypFkPzlM/uzg4OKBECX29DYxXrlw50vWPHj3CuXPnJGUjh6qqGDlypNCf+eDgYFSpUgUhISHo0aMH/vzzT0RFRVn8lBBrMHr0aKGuyC8e+t2/fz9mzJihZVoAnnZ1bNy4scHX165dm7zHnj17yDHWaPny5UZPTilfvrzR0wIskaOjI5YuXYoJEyYIr3H+/HkMHjxY08/kTL/du3fj4sWLQrExMTH47bffNM4oc8nJyeTPH1WqVBGaUMOYNeICBMOJTB9o2bIlvL29M32tR48e5IZADx8+xPLly8l5AE+nBlELKACgTZs2cHR0JMW0bNkShQoVMvj6nDlz4tNPP6WmZhOoBQgpKSm4cuWKpGyYraMeZsuVKxe5AMleuLq6Yv369QY3LylXrhw++eQTyVkxJoeDg4NQA40X3bp1C+3bt0dKSopGWTFLI1LomhUfHx+sX78eVatW1WxNxsylVq1a5ELr6Oho8lkBxph2+K7wy/QVIFRUFEXK7FdFURwBVNBzmUUWIPxLX25VJO6tb21L/vfGGLNiI0eOhKurq1FrTJkyxaRd7OwddWR2vnz5+IaxlSlatCi5A3dMTIxQV39zUVUVoaGh6NSpE7y8vFCzZk307t0bgwcPRrNmzeDn54evv/4aqampmu67ePFiTdYZN24chg4dqtmBJ5FOwHXq1IGLiwv++OMPLF682KjD/+Hh4a89kBYpQKhRo4ZwDkxcgwYNyDEyJnlYIuoEBG9vb6OmQ5kKdQrC48ePcebMGTnJGIB6ALB48eIm+e9ALUAAYPRhYlN6/PgxWrduLTSy+sMPP8Thw4dx5MgR7N69G0uWLEG7du2EDs2z1wUGBqJbt27kuJ07d+LcuXN49OgR3nnnHc0PXnt4eGDq1KmkmIIFC5IPUB06dMjmH6ZkZGQYdbj+mYkTJ9rsgWhHR0d89tlniIqKEl5jwYIFGDhwIHcENiFjirkB4MsvvzTJ4ZPjx4+T71PxIQdmTwICAsgdbfft22d3RV+JiYlCEwSzmwSQN29eoUYSmTWO0Cc9PV24cdDHH39MjnFycsK3335r8PWjRo1CgQIFyPvYApGC+7Nnz0rIhNm669evIy4ujhRTs2ZNm5rAprUCBQpg06ZNCAgIyPY6Hx8f/PXXX3BxcTFRZoxpb/DgwUafIYiMjBT6XMEs39mzZ42evKzT6dC8eXNMmzYNsbGxaNiwoUbZMWZeOp0OHTp0IMWkpaVJmXbMGDOMbT6FEXcEQHZP8dwg7yB9NQA5s3n9EYCjkvbWgr6TX/Uk7l1fz+v2MZ+eMWZyhQoVwhdffGHUGnFxcQgNDdUoI5YdVVXJBQjVq1cnd6xi5icyBWHz5s0SMtHe2bNnUblyZbRq1QqrV6/G3bt3X7vm3r17+PLLL/HWW29pVoSQkZGBFStWaLIWAEyfPh2DBg3S5AG8SCfBunXrAnjaiaZnz57Yt28fUlNTkZSUhPLly5PWUlX1tXHcIjlRR1AzbYgUIBh7Y9haUCcgUIu/zKVyZX0D9F53/PhxCZno9/jxY/JBiTJlykjK5mXUn5UArOoG8JgxY4Q/o7ds2VLjbNirPvvsM6G4RYsWYeTIkTh9+rTGGQHffvstChYsSI6rU6cO6fonT57Y/PtQaGgooqOjjVqjVatWaN68uUYZWa7y5cvjxo0bwvGzZ8/Ge++9Z3eHYs1BVVVs2LDBqDUuX75skimaIj9juACB2RNFUcjNSq5fvy48AcVa7d27l1zkVrhwYbRo0SLba7IrUMhKREQEuYBrz549uH79Onmvjh07Ijg4mBwHAL179zZomlaLFi2EPw/bAmoBL8AFCEzMq/d7DcHNrPQLCgrCkSNH0LZt20xf79KlC8LCwuDr62vizBjTVpEiRTBy5Eij11m4cKHdTMO0J6ITup5ZunQp0tPT8ddff2HIkCFwd3fXKDPGLIPI5OAjR45IyIQxZgguQHiBqqqPAOj7Nmn4LHUafSfl9vybn6Xaruf1uoqiaF7yryiKCwB97zzbtN6XMcaeGT58OCpU0DfAJnszZszQKBuWnYSEhEwPa2dHpKMSM79mzZqRYzZt2iQhE22dP38e9evXN/gg7Jo1azBkyBBN9j548KDmUyJmzZqFESNGGLWGqqrkUdgFChTI9GGlo6Mj8uTJg7fffpucx4tTGNLT03Ho0CFSfFBQEDw9Pcn7MuPVqVOH3JX86FFLrgvXhqqq5AkI/v7+UnLRWsWKFckx5ipA+Pvvv8kdiE1VgFCqVCnynx1rmYAQGRmJSZMmCcW6uLgIFTYxmtKlS6N9+/bkuPHjx0v57lWjRg28//77QrHUAgRAbPqTtVBVFd98841RawQGBuK3336DoigaZWXZChQogB9++EE4/pdfftFk4gTLXmxsLLm4MzNaTaXLjsgDWy5AYPZGZIqiSKMCaxYZqW/g/Ov69u2rtxFMSEgIihUrRlr3yZMn5Ps0Ik04nJychCaoPaMoCtavX4+BAwdmeU3fvn3x+++/2+yUJ0OIFCCYc6ohs17Ue84AUKtWLQmZ2J68efNi7dq1uHbtGhYsWIApU6Zg8eLFiImJwfLlyxEYGGjuFBnTxPDhwzW5Z25sM0RmWVRVFZrQ9czkyZOFpsMyZk1Eirrt4dktY5bKfu9QZE3fYXXanBfD6ZsbulXSvlo5AOB+Nq/nAtBUwr4tAGQ3u+wuANqdRcYYI3BycsIvv/xCPoD1op07d1pVR1hrRZ1+ADw9FMysT6NGjciTK7Zv344nT55IykgbAwcOxJUrV0gxs2fPxtq1a43e29huHFmZMmUKlixZIhx/4cIF3Lx5kxRTq1atbA+kde3alZzHix1oYmJi8PDhQ1K8yOEFpo3cuXOTO+IfP36cfCjc2ty4cQOPHtHq361lAkLJkiXh7EyrjTdXAUJMTAw5xlQFCDly5CAXalpLAcLkyZOFu4E3bNgQOXNmN9ySaWXUqFHmTgEAkCtXLsydO1f4EJhINydb7ny3e/duHDx4UDj+rbfeQnh4OPLmzathVpbvo48+Eprw88z48eNx+fJlDTNir9q4caMm60RGRkrvok6dgJAvXz74+flJyoYxy8QFCPodO3aMHPPOO+/ovUZRFPTr14+8dnh4uMHXpqWlYfXq1eQ9Bg0aZPShWRcXF/zwww84ePAgvvrqKzRp0gQdOnTAp59+ikOHDuGXX35Bnjx5jNrD2uXPnx+5c+cmxfAEBCaCOgFBURTUqFFDUja2qVChQnjnnXfwySefoGfPnihdurS5U2JMU66urpg2bZrR64SHh0uZ5snMIzo6GqdOnRKK7dKlC4YOHapxRoxZnsDAQHh4eJBieAICY+bDBQivW6Xn9cqKomh6GlFRlLIAyum5jH63y4RUVU0DoO90W3cJW+tb889/c2OMMWkqV66MYcOGGbWGMR0LmWG4AMF+5M6dm9xt6P79+0JdjUzl5MmTwlMa3nnnHaOmF2RkZGDlypXC8fq8//77iIqKEooVeYCvbxS2t7c3+QDv0aNHkZycLJwTP5wyrypVqpCuf/jwoc13z6NOPwCsZwKCk5MT+c/48ePHhQ+kGyM2NpYcY6oCBAAoX7486fq4uDjcv59d3wLzi4+PN+o9r2XLlhpmw7ITHByMhg0bmjUHBwcHrFq1CuXK6bull7XAwEAUKlSIFHPgwAGz/EwyBdHpBwMGDEBkZCSWLVsGb29vjbOyfI6Ojvjtt9+QI0cOofhHjx5h9uzZGmfFXrRhwwbN1pLZwOLu3bvkeydVq1a1m4kjjD0THBxMbnxhyfecZKBOQChRooTBh/e7dOlCzodSgBAeHk5udpEnTx5NuxNXq1YNo0ePxpYtW7B69Wp8++23PG3mX4qikIvhuQCBUaWkpJB/jpUrV45cHMMYs33t2rVDixYtjF5nwYIFGmTDLMEff/whFNevXz8sXbrUridhMfuhKAr52W1MTAxSUlIkZcQYyw6/M71CVdXzeNrNPztZz78UM0jP63tVVTV+RrN8v+t5vaOiKJo9BVQUpQiAtnou05cTY4xpYuzYsXoPtWZnyZIlSExM1DAj9iouQLAvzZs3J8eIHvA3BWMmECQlJaFXr17CB9X27t2Lq1evCu+vT0pKCjp06ICkpCRyrMhhf0M6FVK7Eaempj7vFHrggL6vEmI5MXmoN7EA2x/leeEC/euntUxAAIAKFSqQrk9ISMD169clZZO1c+fOka53dHRE8eLFJWXzOpFD1yJTHUxp1qxZwhNOFEXhAgQTe++998y2d/369bF//340a9bMqHUURUGdOnVIMUlJSUKfmyzdpk2bsGPHDnJcz549MXPmTFSqVElCVtajTJkyGD9+vHD8vHnzyNOPmGESExM1PXgcHR2t2Vqvoh60A8AHYpldypkzJypWrEiKiYqKet64wNYlJCSQJ+tQ3seLFi0KX19f0vr79u1DamqqQdeKFCR36dLF7icTmBL1e+/58+dtfpIl09aRI0cM/pnxDLUhEmPMPiiKgiVLlqBUqVJGrbNowvZ94gAAIABJREFU0SKDprinp6djyZIl6NixI7y8vODn54dq1aph1qxZ/J3fAqiqKlSA0KhRI8ybNw8ODg4SsmLMMlGf3aanp0tt2sEYyxoXIGTuFz2v91UUpbAWGymK4gugl57LftViLxPYDiC79rpOAIZruN8IANm1mYkHsEvD/RhjLEsuLi5Yv349qlWrJhT/+PFjLFq0SOOs2IuoBQhubm4oXFiTt3tmBiIFCBs3brTYbrLbtm0zKj4sLAxr1qwRijWm+MFQcXFx6NmzJzIyMkhx1AIEJycng25YUAsQACAiIgKqqiIsLIwU5+bmZtKO5ex1IgUIIoezrIktT0AAQD4sBDydgmBqcXFxpOuLFSsGZ2dnSdm8jjoBAZDbtdlYDx48wNy5c4Xj27VrhzfeeEPDjJg+bdu2NelBr4CAAAwfPhyHDh3Cjh07NDt0K/K5Q+TntCVLTU0VHiE/cuRIjbOxXkOGDBH6/QQ8PSQv2oWPZW/Tpk3k7znZkVnM96yomoILEJi9ojYSSEtLw5EjRyRlY1mOHTtGjqlcubLB1yqKgrp165LWT05ONuh7fFpaGlavpg+kF5nKwMRRCxBSU1Nx8eJFSdkwW7R3715yjDENyhhjti1v3rzYtGmTUU0sEhISsH79+myvuXPnDlq3bo1evXphzZo1SExMRHx8PA4fPoyBAwciMDDQ4pvD2LrDhw+T7/kDwJo1a3jyILM7wcHB5Bh7+c7NmKXhAoTMLQaQ3XzNnAC+1Wiv7wC4ZPP6jX/zsXiqqqYDmK7nso8VRTG6nbSiKKUBfKjnsu//zYkxxkwiX7582L9/P+bPny8U/9NPP1ns4WdbQC1ACAoK4i/zVqxcuXLw8fEhxZw6dYp8eNwUkpKSNPnC/N1335Fj0tPTsWrVKqP3NsRff/2Fr776yuDrHz58iKioKNIelSpVgotLdh+9nxI5uLVnzx5ERkaSO8dXq1aNu5aYWZkyZciHtnkCwst0Oh25A6U5iRQgUH/eaOH8+fOk6wMDAyVlkjmRCQgnT56UkIk2Fi5ciLt37wrFurm5kd7DmDZcXFzQrVs3aeuPHTsWly9fxuHDh5GUlITz589j0qRJqFq1qqbfE0SmrtnaAao5c+bg9OnT5Li2bdtyIecLHBwcsHjxYnh5eQnFz5w5k+9JSBAaGqrpejIPjBw6dIgcI/JAmDFbIDLJUGSKozUSKUCgTjKiFiAAQHh4uN5rwsLCkJCQQFo3f/78CAkJIefDxIlM/ouNjZWQCbNVItOreAICYyw7fn5+2LRpE3bv3o1PPvlEaI158+Zl+dqdO3dQu3btbCe9X716FU2bNkV8fLzQ/sx4IvcHOnXqBHd3dwnZMGbZeHo9Y9aDCxAyoarqIwAz9FzWW1GU9sbsoyhKZwDd9Vw2XVXVx0bu468oiqrnr7HG7PGCuQBuZ/O6E4AliqIIt2VUFCUHgCXIfvrBbQBiJ4AZY8wIOp0O/fr1y/YLflbOnj2LXbt4cIsMqamp5I4CIgeBmOVQFEWom8iUKVMkZGOcnTt3atIx8/Dhw+TDKuHh4bhx44bRextq3LhxBt+AO3LkCHl8uqEHBPz8/MiHqfft24dly5aRYgCgRo0a5BimLWdnZ/JB6mPHjmnaydbSUDtrFylSBE5OTnKSkUCkc7+pJyDcu3cPt27dIsWYugDBz8+P/PDDUgsQVFXF7NmzhWLd3NywevVqlC1bVuOsmCHefvttKetWqlQJn332GXx8fBAcHCx10oKfnx85xpYmINy6dQtjx44Viv3ss8+0TcYG+Pv7IzQ0FDlz5iTHRkZG4sCBAxKysl+qqhp04JUiNjaW/D3IEKqqkg9H+/j48ORIZre4ACFrIhMDLaUAYcWKFeR1O3bsCEfH7B5XMq2VKFGCHMOfcRgFtRlQoUKFrGoyKGPMfEJCQjBlyhShyePbtm3L9Fl3amoqOnXqZNDzvytXrqBHjx7cfMBMtm/fTo556623JGTCmOULCAgg35PnCQiMmQcXIGRtOgB9pZ+LFEWpJrK4oig1APyi57J46C+EsCiqqj4AMEbPZcEAFiqKQv79pyiKA4BFAPTdjRz9by6MMWYWTZo0ETrA/vPPP0vIhsXFxSEtLY0UI/Igg1mW5s2bk2NCQ0Nx6tQpCdmI27Ztm2ZrLVy4kHS9yINXY/Xp0wf37t3Te51IJypDDwgoikKegnD//n1MnTqVnBN3x7IM1E4a9+/fx7lz5yRlY37UCQjW9pA1b9685AO/pi5AEBnFbOoCBEVRyMU7J06csMgHXDExMeRpWc+cPn0aTZo00TgjZqiqVauidOnSmq6ZN29erF69mjwdR5S9FyCMGTMGd+7cIcc1aNAA1atXl5CR9atWrRp2794tFDtz5kxtk7Fzly5dwtWrVzVd8/Hjx+QpSYa4ePEiOdeqVatqngdj1sLPz49cgLNv3z6L/CysNeoEhCJFipCn95QsWZIcs2fPnmwLuNLS0rB69WrSmgDQpUsXcgwzjsh9e5H7iMw+3b9/H9euXSPF1KpVi6dpM8ZI2rdvj4IFC5JiVFXF999//9qv9e/fHzt27DB4nYiICPz222+kvZnx7t27h4MHD5Ji3Nzc0KJFC0kZMWbZFEUhP7s9deoUkpOTJWXEGMsKFyBkQVXVZAD6Zl+5A9iqKEorytqKorQFsAWAm55Lh6qqmkJZ20L8COCEnmu6A1ilKEpuQxdVFMUDwBoAXfVcGgWAT/AyxsxKp9NhwIAB5Lg1a9aYtNu4vThz5gw5hicgWL9GjRoJHRybNm2ahGzEiXTEyMrUqVORmppq0LWiD163bt2K48ePw9PTkxwLAImJia/dRM2MSOdASodCagGCiFy5cqFevXrS92H68SjP/2RkZODixYukmKJFi0rKRp6KFSuSrj9z5gwePnwoKZvXiRwsNHUBAgByAUJSUpLmBzG1IDL+Gnh6CNTHx0fjbBiFoijo27evpustW7bMpD/XXF1dyQ+dbaUAITo6Gj/99JNQ7OjRozXOxrZUrVoV/fv3J8etWbNGqCCEZU7WYcfo6GjN1xTJlYuAmD1TFIU8BSEhIQGXL1+WlJFluHfvHs6ePUuKqVy5MnkfRVHIUxDu3r2b7US2Xbt2ITExkbRmwYIFhaYxMOPkyZMHxYoVI8UcOnSI3KCI2SeR+zH8mYgxRuXk5CQ01fOXX3556fPKvHnzMH/+fPI6w4cPx+3bt8lxTFx4eDh5mmHr1q3h6uoqKSPGLF9wcDDp+vT0dBw+fFhSNoyxrHABQjZUVV0FYKmeyzwArFcU5XdFUUpmd6GiKKUVRfkDwFoA+g7e/66qKv3ElwVQVTUdQC8Aj/Rc2h5AjKIobyuK4pLVRYqiuCqK0g9ADIA2etZMAdDr3xwYY8ysevXqhZw5c5Ji0tLSsGDBAkkZ2S+RjrZcgGD9PDw80L59e3Lc4sWLyYdvZblw4YLm3S2dnZ0NGju+a9cuJCQkkNb28vJC/fr1UaFCBfzxxx/Q6cS+bkyfPj3bQglVVcmj0729vVGkSBGDrzdFAUKbNm3I7xNMDpEChMjISAmZmN+1a9cMLlR6xtomIABAhQoVSNerqprtYRWtifzsDwgIkJBJ9qgFCMDTKQiWRqQAYcKECSbrkM+y9+6775IP8Gfl008/RdOmTTVZi4L6c9QWChBUVcWQIUOQkZFBjm3bti0XcRpg1KhRcHR0JMU8fvxYqAiZZU6kaNoQMTExmq8pUoDA09yYvaMWIAAgH863NlFRUeSYSpX0DTzPnMjB//Dw8CxfE5kC2qlTJzg4OJDjmPHefPNN0vUPHjyQUsDHbI/Iz+mSJbM9IsIYY5nq168fOSY5ORlz5swBAFy/fh1Dhw4V2jshIQGff/65UCwTI9Jszhz3KBmzJCLPbrds2SIhE8ZYdrgAQb8PAJzWc42Cpx39YxVFiVQUZZaiKJ8oivKBoijDFEWZrSjKcTw9QK+vez/+3e9D49I2L1VVTwAYaMClvgAWArimKMoaRVHGKIrysaIoAxRFGasoyp8ArgOYD8CQtoIDVVU13akQxhjLhoeHB7p160aO++mnn7gbj8ZEChBERjkzy/PJJ/oGWr3uyZMnqFixIrkThQwTJkyQsm5ISAiWL1+e7TUiD147duz4/JBT48aNMX78eKH87ty5g4iIiCxfj4+PJxdH1KxZkzQKu2zZssid2+BhXUK6dOkidX1muLJly8LJyYkUc+TIEUnZmNeFCxfIMfYwAQEAjh07JiGTzMXFxZGuVxTFLP8dypcvT44xZSGHIZKSksiHLl1dXfH+++9LyohR5cmTR5MJVrlz58aIESM0yIiOWoBgKcWyxtiwYYPQw1cnJydMmTJFQka2x8fHBx06dCDHLVmyREI29knWBARLKEBwdHQkd6JjzNZUq1aNHCMypdWaiHxnEpmAAGhbgJCamoo1a9aQ1+P7OuZDLUAA5BUGMtsiUoBQvHhxCZkwxmxd8eLFhZorzJw5EykpKfjqq6+Mmtj7888/2/xnU0sicg+sYcOGEjJhzHqI3HfatGmThEwYY9nhAgQ9VFV9AKApgHgDQyoB+BjAFAA/AZgMoD8AQ9srxgNo+u++Vk1V1fkAvjLw8jx4OhFhLIBZAGYCGAOgHfRPi3hmrKqq3DacMWZRPvyQXk926dIlrFu3TkI29otagODr64tcuXJJyoaZUtWqVYUeSN65cweOjo4YNWoUkpOTJWSm38mTJ4VGpxriyZMn6NGjR5Y/a0QfvHbt+nKt7ciRI9GuXTuhHDdu3JjlayIHv6mdCR0cHKR29HR3d0ezZs2krc9ocuTIgbJly5JiIiIicOXKFUkZmY9IAYI1TkAQKUA4evSohEwyR52A4OPjAxeXLAcLSkP9cwNYXgHCli1byEWHTZs2Rb58+SRlxER069YNs2fPNmoqxaBBg5A3b14NszKcn58f6fq7d+/izp07krKR7969exg0aJBQ7ODBg1GsWDGNM7JdAwYMIMfs3r0b8fGG3gpnWUlOTsbx48elrK11B+cHDx6Qu5ZXrlwZrq6umubBmLUR6Xht6xMQRCYFihYglC9fHh4eHqSY8PBwqKr62q/v3LkTt2/fJq1VuHBhngRjRiITSGQVBjLbQv05rdPpzDKRkjFmG0TOESQkJGD06NGYO3euUXurqorZs2cbtQYzzLVr18iNBEqWLAlfX19JGTFmHfz9/VG4cGFSzPHjx3Ht2jVJGTHGMsMFCAZQVTUeQEMAtBMAdOcANPh3P5ugquoYGF6EYIxxqqqOM8E+jDFGEhwcLPQQZebMmRKysV/UAoSgoCBJmTBzGDZsmHDsxIkT0bRpU5Mf8Lp586b0w+np6eno0qULduzY8dpr27ZtIz94LViw4GvFHoqiYNGiRUITRbQuQKhevTo5RqR4xVBt27Y1y2FhljXqKM+0tLTn445tiUhHXWucgODv708+rGLKAgTqBARzPezOmzcvihQpQoo5ceKEpGzEhIaGkmNatmwpIRNmDEVR0L9/fxw4cAC9e/cmx7u5uWHw4MESMjOMSCHXP//8o3kepjJ06FChKQ4FChTAF198ISEj21W7dm2hYrFly5ZJyMa+HDlyRNp0yzNnziA1NVWz9Q4dOoSMjAxSjEjnacZsTf78+cnfKWy9yyy12Dh//vzw9vYW2svBwQG1a9cmxSQkJGR6n1hkCminTp3g4OBAjmPaKFOmDNzd3UkxXIDADEEtQPDz8zOqEJ4xZt86duwodE9o6tSpmkxx//PPPzMtzmTa2rlzJzmGpx8w9vSef9OmTclxW7ZskZANYywrXIBgIFVVzwGoCkDWT6nNAKqpqiq7yMHk/i1C6ApAxlSHBwA6q6o6VsLajDGmCZHuBWFhYRZ3OMta3b59Gzdv3iTFcAGCbWnZsqVR/00jIiJMPlJ9yJAhuHr1qvR9njx5grZt27524PjXX38lr5XVg9fcuXPjzz//JE8VOXPmTJad86gFCDqdDpUqVSLFAECjRo3IMYZ6dVoEMz9qAQLwdEyxuaakyEI9ZO/o6Ch8YMScFEUhF4lGR0fj0aNHkjL6T2pqKvlgbmBgoKRs9CtXrhzp+tjYWE0PTRojPT1daCRvixYtJGTDtFCpUiUsWrSI/N91wIABZp1qYU8FCKGhoViwQGyA6TfffIPcuQ0dlMqAp+93IkU5ixcv5kMIRpJ5yDE1NVXTLuoiuXIBAmNPf8YWL16cFGPrBQjU7zGVK1eGoijC+4k0jggPD3/p79PS0vDnn3+S1zH1vUL2MgcHB9SoUYMUExcXhxs3bkjKiNkK6mcs6vsAY4y9yNHREUOGDDHb/pcuXbL5z6fmlpiYiKlTp5LjZD6fZMyaNG/enByzefNmCZkwxrLCBQgEqqomqaraDMDbAGgnGbN2E0AfVVWbq6qapNGaFkdV1RUASgFYreGyqwCUUlV1lYZrMsaY5rp16yZ0SGLWrFkSsrE/1M5bAIS6tTPLpdPpMHToUKPW2LZtGw4ePKhRRtnbt28fli5dapK9AODhw4fo2bPn8wNGiYmJWLduHXmd7A7Uly5dGt27dyevWaVKFcyfP/+lw0+qqpILEEqXLk0ugACeHmLMmzcvOU4fDw8PNGnSRPN1mXEaNGhAjklMTMTvv/8uIRvzUFU1y8KfrJQuXdpquz6KTL2Ijo6WlM1/4uPjyR2srKkAITU1lTydSpZDhw4hMTGRFFOpUiWrLLqxN82aNUOPHj0MujYgIAAjR46UnFH2RAoQRCYImNuVK1fw7rvvCsVWrFgRffv21Tgj+9CtWzfy4c6YmBhuimCk/fv3S11fy88ke/fuJcfUrFlTs/0Zs2bUe4hxcXHSpqOY28OHD8mfrcuUKWPUniIFCGFhYS/9/b59+5CURHs86+3tzYVYFkDkvUj2+zOzbvfu3SM3sypWrJikbBhj9uKdd96R8hzKUNu2bTPb3rYmJSUFR48exYkTJ3D06FF06dIFXl5eOHbsGGkdnU6HevXqyUmSMSvTqFEj6HS0481bt27VZEoMY8wwXIAgQFXVRQACAHwMIFZwmVP/xhdVVfU3rXKzZKqqXlZVtROAKgCWAEgRWCYFwGIAlVVV7ayq6mUtc2SMMRnc3NyEDkosWbKE/PCDvU7k0ESpUqUkZMLMqU+fPkIHu140Y8YMbZLRwxzFR8ePH39+k3Hp0qV48uQJKd7b2xu1atXK9ppWrVoJ5fbee+9h/Pjxz/8+Li4Od+7cIa0RHBwstLeDg4OUMaft27fn0dwWqESJEkKTMqZPn24zHYIvXryI27dvk2KoUwQsicjUi6lTp6Jt27YoUaIE8uTJgzfffBOff/65pl2I4+LiyDEBAQGa7U9Vvnx5coxIgagMoaGh5JiWLVtKyITJMH/+fL2fT1xcXPDHH3/Aw8PDRFllzs/PjxxjbRMQHj58iDZt2uD69etC8dOnT7fagjdz8/X1Rf369clxK1eulJCNfVBVVeoEBACvTbETlZGRQT6M+cYbb8DX11eT/RmzdtQChLS0NKt7DzfUpUuXyDEin4FeVLlyZeTMmZMUExYW9tJ3+I0bN5L37dy5M/kQDNOeSBGI7PdnZt1E7u3wBATGmLHc3NzQv39/s+2/fft2s+1tCxISEjBr1iy8+eabcHNzQ3BwMCpUqIDg4GDh+ypVq1ZFnjx5NM6UMevk6emJ6tWrk2KSkpJw6NAhSRkxxl7Fd0cEqar6UFXVOaqqlgYQBOB/ABYBOALgGoAHADL+/d9rAA7/+/r/AJRQVbXMv/HJJsj1H1VVFT1/jZWdxwv5RKqq2gtAPgCtAEwGsAnAeQC3AaT++9dtAOcA/PXvNa0A5FNVtbeqqrQSUcYYM7OPP/6YHJOSkoI1a9ZIyMa+iBQgiBykY5YtR44cmDlzplFrbN++Xdoh44yMjOdfhpcvXy5lD32WLVsGAPjmm2/IsYY8eG3YsCFy5MghlNuXX36JVaueDr2iTj8AxAsQADljTrObFsHM63//+x855tSpU1i/fr2EbEzv6NGj5BiRQ/yWQqR44o8//sD69etx9uxZ3L17F/v378eECRMQFBSEdu3a4fDhw0bndf78eXKMNU1AACynAGHXrl3kGC5AsB4uLi4IDQ3F8OHDM/0MEhISgpMnT6Jq1apmyO5lOXPmRIECBUgx1nR4MSMjA7179yZP2XmmY8eOCAkJ0Tgr+9KzZ09yTHh4uIRM7MPp06dx69YtqXtoVYAQGRmJu3fvkmK46zdj/xGZonrmzBkJmZhffHw8OeaNN94wak9nZ2dyF/wrV6689DlKpAChS5cu5BimverVq5OnPG3dulVSNswWcAECY8xcBg4cKPz8zFi7du2y2QldMiUmJmLQoEHw9fXFwIEDsX//fmRkZGiytozGaIxZs+bNm5NjNm3aJCETxlhmuABBA6qqnlFV9QdVVd9WVbWqqqreqqq6q6rq8O//equqWu3f139QVVW71ohWTFXVFFVVQ1VVHaGqagtVVYupqppPVVXnf//Kp6pqcVVVW/57TaiqqiJTExhjzOyKFy8u9MGYOw4aj1qAkD9/fhQsWFBSNsycWrVqhfbt2wvHJyQk4NSpUxpmBFy/fh1Dhw6Fm5vb8wp+kRtUiqKQH7i96tdff4Wfnx9u3LhBju3Tp4/ea3LlyoUGDRqIpAbgaSHX7du3TV6A0LhxY+HYzHh6evLNQwv21ltvkQ+AAsCYMWM0u7lsTiIHQ615AkKxYsXg7u6uyVqqqmLdunWoUaMGhg0bRp4k8yJrK0AICgqCk5MTKUakQFRrKSkp5IIRLy8viziszgzn4eGBSZMmIS4uDuvWrcPUqVOxcOFCnD59Grt27UKxYsXMneJz1A7A1lSAMGfOHOHiehcXF0yePFnjjOxPhw4d4OLiQoo5fPiwUe9n9mz+/PnS99Bq+pLIfSd902UYsyciB0+1nJ5mScxRgAAAdevWJcc8K7I7f/48YmNpg+4LFSqEGjVqkPdk2suTJw9Kly5NiomKirLZIiBmPC5AYIyZS8GCBc3W9OTevXuaNNWxJ5cuXULt2rUxc+ZMKfdN+BkiYy9r1qwZOUZk+jVjTAwXIDDGGGMmMnDgQHLM9u3bkZiYKCEb+5Ceno7o6GhSTPny5Y0+yM0s14wZM5ArVy7heJFOyVk5cuQIihcvju+//x4pKcbVWE6fPh23bt0y6qA9IPawuGLFiqhUqZJB17Zq1Yq8/jM3b97E8OHDyR3aHR0djZpqEhAQgKJFiwrHv6pDhw7kg7rMdHLkyIGPPvqIHBcVFYW1a9dKyMi0qH++dDodKlasKCkb+XQ6neYFFBkZGZg6dSqGDRsmvEZcXBzp+ty5c8PT01N4P2M5OzujZMmSpJg9e/aQuy1r7fDhw0hNTSXFNG/eHA4ODpIyYjJ5e3ujTZs2GDp0KN5++20EBQVZ3Gd+f39/0vUXL16Uk4jGEhISMHr0aOH4iRMnavpZzF55eHigdevWpJhHjx7h2DEeQkv18OFD/PLLL+Q4Hx8f0vXnzp0zekKfqqpYsWIFOY4nIDD2H5GDp7Z6+NkaCxBEDqW0bNlS7xRQZjoi70ki733MPlALEBwcHPi7CmNMM926dTPb3tu2bTPb3tbm3r17aNasGU6fPi1lfU9PT9SpU0fK2oxZqypVqsDLy4sUExkZicuXL0vKiDH2Ir5DwhhjjJlI06ZNyQ+l0tPTbeJAo7nExcUhOTmZFGPMQWVm+YoUKYKvvvpKOF6rAoQLFy6gZcuWePDggdFrffTRRxg0aBA8PT0RERGB6dOna5Ch4fr27WvwtcZ2cPnll1/I/w3Kli0LV1dXo/Zt1KiRUfEv6tKli2ZrMTk+/PBDODs7k+OsfQqCqqrkCQilSpVCzpw5JWVkGrImOMycOVP4Mxx1AkJgYKDZD1KXK1eOdP29e/fw448/SsrGMHv27CHHaPl+wNirqAUISUlJZi/kMcTcuXNx584dodh69eph0KBBGmdkv0SmMu7du1dCJrZtyZIl5N/zzs7O6Nq1KykmOTkZV69eJcW86ujRo+RpKrlz5+b7Joy9wMPDgzxJ1VYLEC5dukS63tXVFfny5TN63+rVq5MbPWzbtg0ZGRnYuHEjeT9jmmsw7YWEhJBjuACBZYVagODv78+NZhhjmmnZsqVm03qpuADBMOnp6ejevTtOnTolbY8+ffrwewtjr9DpdGjatCk5bsOGDRKyYYy9igsQGGOMMRPR6XR49913yXErV66UkI19OHHiBDmGH6TbvsGDB6Njx45CsWFhYUYfMFZVFe+++y5u3rxp1DoAkC9fPsyePfv53+fIkQP/+9//kJqaapLfy87OzujRo4fB1/v5+ZEPqRrL2KkQANC4cWMNMgG8vLxQv359TdZi8hQqVAhvvfUWOS46OhqrV6+WkJFpXL58GQkJCaQYWYf3TalKlSrS1h4xYsRLHfbv3Lmj99+xqqrkCQiBgYFC+WlJ5D1HiwlAxhApQKhdu7aETBh7ilqAAFj+FISMjAyhTvAA4O7ujoULF3KHYQ3VqlWLHLNv3z4JmdguVVUxc+ZMclzDhg1RpkwZchz1kNyrRA5gtm7dGo6Ojkbty5itoTacsdUCBOoEhDfeeEOTQmpXV1dUq1aNFHPp0iV06dKFfNjO2dmZi5ItTIsWLciH9E6ePInY2FhJGTFrdu7cOdL1IlNwGGMsK66urmjXrp1Z9j5w4ADu379vlr2tyWeffSY0QYvi/fffl7o+Y9ZKpLHK+vXrJWTCGHsVP8FhjDHGTKhz587kmB07diAxMVFCNrZPpADB1IejmenpdDosXrxYqJtqYmIioqOjjdp/zZo12Llzp1FrPPPNN99k+rDW0dERx44d02SP7LRt25bcrc7UEwC0KEBo0KCBJg/FO3bsyId1rMQnn3wiFCd6yNISHD16lBwj8/C+qcj8Zzh79ixq1qyJNm3awNPTE3mz3mWGAAAgAElEQVTz5kWBAgVQtGhRDBs2LNOHOjdv3iQ/7AkICNAqZWEih1pv3rxptj8z6enp5EO13t7eKFq0qKSMGBMrQKB2Lje1PXv2kIuqnpk+fbrQvxOWteLFi5M/u+/duxeqqkJVVcTExGDGjBkYOHAgevbsiYkTJwp9frBlu3fvRkxMDDmub9++QgfYjClAUFVVqOGFyH0txmxdiRIlSNdfunTJrIW4sogUIGilbt265BiRBgL169eHm5sbOY7JkzdvXjRp0oQcx1MQ2Kvu3LmDW7dukWK4AIExprVu3bqZZd+0tDSegqDHli1bMHnyZKl7hISEoGTJklL3YMxaNW/eHA4ODqSYnTt3cnEVYybABQiMMcaYCRUtWpR8GDYtLQ1r166VlJFtO3nyJOl6nU6H0qVLS8qGWRJXV1fMmDEDffr0Icfu2rVLeN+UlBThg82vcnd3z3b6gE6nEyqyoOjbty85pn///siVK5eEbDKnRQFCvnz50LJlS6PX6dq1q9FrMNMoX748OnXqRI7bvn271d7MioyMJMfYQgFCiRIlpB4gOXr0KDZs2ICkpKTnv/bPP/9g6tSpKFWqFKKiol66/u+//ybvYQkTEGrWrInChQuT4yZNmoRHjx5JyCh7J06cIP9ZrVOnjibFaIxlxc/Pjxxj6QUICxcuFIrr16+f0OdMlj1FUfDmm2+SYq5fv47evXsjICAAZcuWxeDBgzFr1iz8/vvvGDVqFIKDg9G1a1fyYS1b9dtvv5FjfHx80K5dO5MXIBw9epT8M8Td3V1o5D1jto5agKCqKs6fPy8pG/PIyMjApUuXSDFaFiCEhIRotlZ2WrVqZZJ9GI3I/TYuQGCvEvlcxQUIjDGtNWrUiNw4QCvWPN1ZNlVVMWzYMOn7fPDBB9L3YMxaeXp6ok6dOqSYJ0+eYOvWrZIyYow9wwUIjDHGmImJdP9esGABVFWVkI153b59G2fPnsXZs2el/PNRJyCUKFECrq6umufBLFf//v3JMbt37xbeb/Lkybh48aJw/It69eql99CszO6Ufn5+Qh3GPD09MXfuXAkZvc7Z2Rlly5bVZK0RI0YYdfC0fPnyQh35mPmMGTOG/N88LS1NaPqOJaB2MFYUBRUrVpSUjenodDqz/XNcuXIF7du3f2nSlUgBgiU88HZwcMDAgQPJcfHx8Zg6daqEjLK3Z88eckzt2rUlZMLYf0QKEIydzCXTvXv3hLqrV61aFXPmzOGCH0lEJtYsWbIk24PqK1asQIsWLfDgwQMjMrMNW7ZsIcd8+OGHcHJyQsGCBclFkcYUICxfvpwc06ZNG7i4uAjvyZitEvk8fubMGQmZmE9CQgIeP35MiilSpIhm+4eEhMDDw0Oz9bKiRXMKpr02bdrA2dmZFHPq1CmcOnVKUkbMGon8XLaE+zGMMdvi5OSkyRTxxo0bk2M2bNhA/jxnL06ePCn9HpyXlxc6dOggdQ/GrF2bNm3IMevXr5eQCWPsRVyAwBhjjJmYSEfl/fv321R17ubNm9GwYUPky5cPJUqUQIkSJVC0aFGMGjVKsy64Dx48IHcTK1++vCZ7M+tRuXJluLu7k2LCwsKQkZFB3uvmzZv49ttvyXFZ+eijj/Re8+abbwp1pDbE119/TR51+Ez37t2xaNEi5M2bV+OsXlauXDnkyJFDk7Xq1Klj1H+/7777TvjfFzOPsmXLCr1nW2sBAnUCQlBQkNTJAaZkzkkOFy5cwJAhQ57/vUgBgqWMZe7fvz9y585NjpswYQIuX74sIaOsRUREkGOo3XUYo3Jzc4OXlxcp5uDBg5KyMd6KFSuQnJxMjlu9ejX5ABkzHHUCgqEOHz6MUaNGSVnbWly/fh3Xrl0jxTg7O+P9998H8LS4k3qITbQAIT09HUuXLiXHaXEQhjFbRJ2AABhXQGSJ4uPjyTFaTkBwcXGRPnWyTJkyKFq0qNQ9mBgPDw80b96cHBcaGiohG2at9u/fT44pVqyYhEwYY/ZuxIgRRj3XcnNzw4IFC8j3jO/fv49t27YJ72vLdu7cKX2Pt99+W7PnmYzZKpEChNDQUKSlpUnIhjH2DBcgMMYYYyZWtGhRBAcHk+NGjx5tE1MQJkyYgObNm7/2Zf3ixYuYOHEiatSooUmHeJFOBFyAYH8cHR3JBwqTkpKEDi7+9NNPSElJIcdlplmzZgZ19tfpdOjYsaMme77ogw8+QM+ePY1ao3fv3jh37hwmTZqkUVav0/qA1fDhwzF+/HhSjKIomDVrFpo1a6ZpLsw0RDq6W2MBwtWrV3H9+nVSTOXKlSVlY3ohISFm3X/JkiU4duwYAOD06dOkWHd3dxQqVEhGWmQeHh74+OOPyXHJycno3LmzyT7nqqpKnoDg4eGh2UQdxrJTunRp0vXR0dEW23V+0aJF5Jh33nlH027I7HXBwcFwcnKSsvaCBQtw+/ZtKWtbg2fv5RRdu3ZFgQIFnv89tQDh/PnzQsXxu3btwtWrV0kx7u7uQhPwGLMHgYGB5Mk9sbGxkrIxD3MXIABPD23J1LZtW6nrM+OIFMn99ddfEjJh1oo69djR0RH+/v5ScmGM2Td/f38MHz5cOH7u3LkoUqQI2rVrR45dtWqV8L62LCwsTOr6efLkwbBhw6TuwZgtCAwMJN8/T0xMFCo0ZYwZjgsQGGOMMTMQuSF++PBhq+/KM3/+fHz++efZXhMVFYWePXsKPUR/kcgBUC5AsE/169cnx/z222+k6zMyMrBw4ULyPplxdnbG1KlTDb5epIN7dj799FPMmTOH/HA9M56enhg+fLi0B35aFyAoioLPP/8cs2fPNuh6b29vrF+/XuhALrMMtWvXJnf5t8YChKNHj5JjzDk1QGtNmjSBi4uL2fZXVRXDhw+HqqrkCQhBQUGa/DzWyuDBg4X+XR44cAA6nQ7jx4/HzZs3JWT2n7Nnz5ILbmrVqsVTbJhJVK9enXR9RkYGjhw5IikbcUlJSdi3bx85rl+/fhKyYS9ydXWVVkSYnJyMDRs2SFnbGogUIPTu3fulv6cWIDx69EhoitCSJUvIMW3btjXr5yXGLJmrqyu5gE7kZ4Yls4QChBo1aiAoKEjTNZ9xcXHBgAEDpKzNtNG6dWvy+1RERATu3r0rKSNmTW7evImYmBhSTNmyZeHo6CgpI8aYvRs3bhxGjx5NinF1dcXcuXPRrVs3AGLPBtevX4/U1FRynC3LyMhAeHi41D2mTJmCggULSt2DMVshMgVh/fr1EjJhjD3DBQiMMcaYGXTu3Fko7ssvv7TaKQiPHz/GuHHjDLo2IiICP//8s1H7iRx44QIE+yRSgLBixQokJycbfP3u3bvxzz//kPfJzLRp00jV/bVr19aka7ObmxtWrVqFb7/9Fjqdtl8jmjZtCi8vL03XBJ4eGJWhf//+2LBhQ5Zdx3U6HQYMGIDY2Fi0atVKSg7MNBRFIR/SO3HihNFFdKYWGRlJjrGlAoRcuXKZvaPvjh07sGHDBly4cIEUJ+uAjagCBQrg3XffFY4fPXo0fH198emnn0r7c7R582ZyTO3atSVkwtjrqAUIAHDw4EEJmRgnMjKS/Gc4KCgINWvWlJQRe5HWRbov2rp1q7S1LZ3IYeJXP09RCxCAp4V1FMnJyVi9ejV5H5FGGozZE+rn8piYGNJ9JUsnUoDg6+uraQ6KokibgjBw4EAULlxYytpMGyKTetLS0rB9+3ZJGTFrItLZ2tzTNBljtk2n0+Grr77Czp078fbbbyNfvnyZXufk5ISKFSviyy+/RHR0NN57773nr1WuXJk8qSUpKQm7du0yJnWbEx0dLW3aY44cOfD7779zQw7GCLgAgTHLwwUIjDHGmBn4+/ujZcuW5Lhjx47h+PHjEjKSb/Xq1aTOfJMnT0Z6errQXqqqkg8+5M6dW/POW8w6VKxYEXnz5iXF3L9/H2vXrjX4eq2mH3Tq1An9+/cnxTg4OGDy5MlG7VuvXj0cPXoUHTt2NGqdrOh0OjRt2lTTNYsUKULuQEjRqlUrxMTEYPLkyWjatCnKli2LunXrYuTIkTh//jxmzpyJ3LlzS9ufmU6FChVI1z948ECzgiNTEZmAULFiRQmZmE+HDh3MnQI6dOhA/uxTsmRJSdmIGzFiBFxdXYXjU1NTMWnSJLz77rtSihBEpu7UqVNH8zwYy4ytFCCIfGft27evRU10sWUyCxC2b99udYWYWqH+vvf393/te6gpChDWrl2LBw8ekGLy5cun+fc1xmxNpUqVSNenp6cjKipKUjamRy1AKFCggFHfGbLSq1cvzZtmlC9fHl999ZWmazI5WrRoQY6x9onTTBsih21FmhoxxhhV/fr1sXDhQty8eRPXr1/HoUOHsGnTJuzYsQPR0dF4+PAhjh07hnHjxiEgIOClWEVRhO55r1q1Sqv0bYJIkdrgwYORkZGB+/fvQ1VV3Lp1Cz///DNatmyJ2rVro3Hjxpg0aRIuXLiA7t27S8iaMdtVrVo1FChQgBRz5swZ8vRxxpjhuACBMcYYM5MxY8YIxa1Zs0bjTExjw4YNpOsvXLiAdevWCe118uRJXLt2jRRTvnx5PvBipxwcHNC+fXty3KJFiwy67u7du5rcsCtatCjmz58v9Pu0adOm+Pzzz8lxb775Jk6cOIGdO3eiRIkS5HgKrQ+0yJp+8CJPT08MGzYMmzdvxsmTJxEWFoaJEyeSu8owyyYyHefEiRMSMpGHOgGhePHi8PDwkJSNebRu3RoODg5mzUGk8NLSJiAATwvARo4cafQ6CxcuxHfffadBRv9JTk7G7t27STE5cuRAcHCwpnkwlhVfX1/4+PiQYg4cOGBxU/JEOsH36NFDQiYsMzI/J9+8eRMnT56Utr6lunfvHs6dO0eKyeywcrFixch7UwsQFi9eTN6ja9eucHZ2JscxZk9EPi+KFIJbqkuXLpGul9UExsfHB40bN9ZsPRcXFyxbtgwuLi6arcnkad68OTlm06ZNdls8yf5DvU+gKArq1q0rJxnGGMuETqdDwYIFUbVqVTRr1gwNGjRAmTJl4OTklG1cp06dyHvZ82TDzFDfI4Cn97gURYGbmxuAp0X977//PjZu3Ig9e/Zg69atGD58OE/YYkyAg4MDWrVqRY7jKQiMycMFCIwxxpiZVK1aFa1btybHUQ8JWoK0tDRs3ryZHDdt2jSh/URujvDIXPvWp08fcsy2bdsMmuqxfPlyPHr0SCSt54oWLYpt27YJH/hVFAXjx4/H0qVLkSdPHoOuX7x4Mfbu3Yty5cqZpDiHOiZdH1MUIDD7YOsFCDdu3MCVK1dIMZUrV5aUjfl4enqiXr165k6DzBILEABg+PDh8PPzM3qd0aNHY//+/Rpk9NSuXbvw+PFjUkz9+vX5wBEzKeoUhGvXrpEmzZkCtQDB29sbvr6+krJhrypcuLDUKQj2eFhBpIt5ZgUI+fPnJ09RoxQgXLt2Ddu2bSOtDzztKM4Yy55IAcKRI0ckZGIe1AkIMqfQfvLJJ5qtNW3aNJQuXVqz9Zhcb7zxBsqVK0eKuX79utVOnGbauHHjBmJjY0kxIhOVGWPMHKpXrw5vb29SzMWLF8lN/mxVRkYGwsPDSTHu7u42Nz2aMUvTpk0bcgwXIDAmDxcgMMYYY2Y0btw4cow1jufev38/7ty5Q47bu3cvDh06RI7bsmULOUbr7uvMutSuXRtFixYlxaiqit9//z3ba9LS0jBz5kzhvBwcHNCuXTtEREQgMDBQeJ1nunXrhnPnzmHw4MFZdkbx8vLC1q1b0bNnT6P3oyhYsKCmh5plHqpi9qVMmTLkIhxreq8W6bpZpUoVCZmYn8g0HHNSFAXFixc3dxqZcnV1xdSpU41eJz09Hd26dUNSUpJwfFxcHB4+fAgA+Ouvv8hrtGjRQmhvxkRRCxAA4ODBgxIyEZOSkoLTp0+TYjI7iM3kGj16tLS1RQ64WzuRqR+Z/b4XeW+nFCAsXLiQPHGpWLFiQj+XGLM3/v7+8PT0JMXYSgHCo0ePcOPGDVKMzAKExo0bCzX9edUXX3yBDz/8UIOMmCmJfH8T+Z7IbMdvv/1GjrHGBhaMMfuk0+nQsWNHctyBAwckZGN9Tp06hVu3bpFiateuDUdHR0kZMcYAoFGjRuSmUfv27UNCQoKkjBizb1yAwBhjjJlRpUqVyIf4rly5gsTEREkZyREaGioc+/3335OuT05Oxp49e0gx7u7uqFGjBimG2RadTofevXuT49atW5ft6zNnzkR0dDR53X79+mHhwoW4ePEi/vzzT3KHkuzky5cP33//Pf755x9MmzYNjRo1Qp06ddCsWTPMmTMHsbGxaNSokWb7UWhVCJQrVy6hrvWMZSZXrlwoVqwYKcaaJiCITFay1QKEdu3akWM6duyo6c9oCj8/P7i6upplb0N06NABDRo0MHqdixcvkg/+REdHo02bNvD09ERgYCDc3Nzg6+uLOXPmkPdv3rw5OYYxY4gc9LWkB8MnT55ERkYGKYY7w5les2bN8MEHH0hZOzw8HCkpKVLWtlQiBQhZ/b6nFiDExcUZVFSQkZGB+fPnk9YGgJ49e5pkIh5j1k5RFPIUhNjYWDx48EBSRqYjMompSJEiEjL5z9KlS4XvbXl6emLGjBn4+uuv+eefFeICBEZx+/ZtjBgxghxXv359CdkwxpgcIoWZlnSfyVxUVcWsWbPIcVykxph8uXLlIn/fy8jI4M/9jEnCBQiMMcaYmYl0HrCmg42AcQUIK1asII3ADQsLw+PHj0l7NGjQIMtu8Mx+iBQgHDp0CHfv3s30tStXruDLL78kr1mqVCnMmzcPb7/9Nnx8fMjxhvL29saQIUOwbds2hIeHY9OmTfjoo4/g5eUlbU99mjVrpsk6NWrU4A4jTFMVKlQgXX/+/HmrOUgiMgHBVjtV+/j4oG7duqSYiRMnIjw8XGoHz6wEBQWZfE8KRVGwaNEiFCxY0Oi1VqxYYfCI3Dlz5qBKlSrYsGED7t279/zXr1y5Qt63ePHi5AIkxowVHBwMnY52y9aSJiAcP36cHGOr7yuW7ocffkCtWrU0X/fx48eIiIjQfF1LRv197+XlleV3PWoBwpMnTxAfH6/3up07d+LChQuktQGgR48e5BjG7BW1UDsjI0PofdPSGPIz6FWyvz+5ubkhNDQUc+fORYkSJfReP2DAAIwdOxYrV67E1atXMWjQIKn5MXlq1qwJDw8PUsyBAwfI3Y2Z9Xvy5AnatGlDjtPpdKhTp46EjBhjTI5q1aqRiyr3798vKRvr8cMPP+Dnn38mx4WEhEjIhjH2KpHPcRs2bJCQCWOMCxAYY4wxMxPpImxNBQjx8fFCHeCfycjIwKhRowy+fuvWreQ9tOq6zqxbQEAA+eBpeno6wsLCMn1tyJAhQgeQ+/bta7cd1mrWrAlPT0+j15FxkIrZN+pEDVVVERMTIykbbVEnIAQEBCBv3rySsjG/sWPHGnxtv379ULx4cQQGBuLw4cNCExSMYekFCADg6+uL9evXa/J7pn///i8VFGQmPDwcAwYMwJMnT4zeDxDrnsmYsXLlyoVy5cqRYo4cOYLU1FRJGdGIdILnAgTzcHZ2xqpVq6QUPW/btk3zNS3VkydPyJ/7KlWqlOV3PmoBAvC0+FWfefPmkdetWbMmF+IxRkCdgACIFYRbmosXL5JjTFHA7ezsjPfeew+nT5/GyZMn8d1336F169YoV64cypcvj549e2L79u1QVRUzZ87EmDFj0KlTJ+TIkUN6bkweJycnNGnShBSjqqrQMwVm3SZNmoS9e/eS4ypVqoQ8efJIyIgxxuTw8PBA6dKlSTGWdJ/JHNatW4chQ4aQ43LlyoXKlStLyIgx9qpWrVqRY/bs2QNVVSVkw5h94wIExhhjzMyoXZUBICoqSkImcmgxymzt2rUG3QzOyMjAxo0byetTH0ow2yUyBWH79u2v/Vp0dDRWrlxJXsvBwQG9evUix9kKJycn9OnTx+h1uACBaY1agABYR7HgrVu3yN0qbf0Gev369Q36OVyoUCFMnDjx+d8XKFAAa9aswe7duzFy5Ei0aNEC3bp1w9ixY9GpUycpuZYsWVLKulqrVq0a9uzZA19fX6PWuXLlSrZFqU+ePME777yj6Q1kLkBg5lK9enXS9Y8ePbKY74jUAoTcuXPD399fTjJMr0KFCmHt2rWaH7bM7DuSrYqJiSEfzMiu6EakAOGff/7J9vWEhAT8+eef5HX79etHjmHMnokUIBw5ckRCJqYlMl2lSJEiEjLJnKIoKFu2LEaMGIH169fjxIkTiIqKwuLFi9GwYUOT5cFMR+R7nDETnJl2EhMTsWrVKqxevRp79+4lT7o21KVLlzB+/Hih2Hr16mmbDGOMmUDNmjVJ16ekpODkyZOSsrFsR44cQffu3YXuMdeqVQtOTk4SsmKMvapw4cKoVq0aKebmzZtCk7IZY9njAgTGGGPMzAoWLIgCBQqQYqzhUOMzWt28//TTT/V+2d+yZQvOnTtHWjcwMBCBgYHGpMZsSPPmzckxmR2u+emnn4T2b9++PQoVKiQUayuGDh0KZ2dn4fjcuXOTJ1kwpo9IAcLx48clZKItkW6bIpObrM2vv/6KQYMGZfl61apVcfjwYeTPn/+lX1cUBSEhIZg4cSJCQ0OxdOlSjBkzBvPmzSN/1jOENUxAeKZMmTLYv38/KlasaNQ6c+bMwaFDhzJ9beXKlQZ1gDZUzpw5+f2EmQ21AAEA9u3bJyETmvT0dPJ31YoVK0Kn41vU5hQcHCzUHT870dHRmk2jsXRaT/0ICAggr6evAGHx4sXkIgk3Nzd07dqVnAtj9qxIkSKvfUfQxxYKEOLi4kjXu7i42P29LyZXs2bNyDGbN29Genq6hGyYIeLj49GkSRPkz58fnTt3RqdOnVC7dm0EBgZi1qxZSEtL03S/H3/8Ubi4oVGjRprmwhhjplCjRg1yzIEDByRkYtkuXryI1q1bIzk5WSi+fv36GmfEGMtOmzZtyDG2MIWQMUvDT3cYY4wxC0CdghAdHa35TVcZ0tPTsXv3bk3W2rt3b6bTFC5duoQ//vgD48ePF+pu1LRpUy3SYzbC29ubPIo0Njb2tWr5TZs2kfd2cHDA2LFjyXG2xtfXF3379hWO//jjj+Hi4qJhRowBfn5+cHd3J8WIHEYzNZEc7aEAQafTYcaMGTh16hSGDBmChg0bonbt2ujevTvWrVuHiIgIUjf/PHny4LvvvtM8T2sqQACe/nw/ePAgxo0bJ7yGqqqYOnVqpq/NnDlTeN3MNGjQgN9PmNlQO9MBllGAcObMGaSkpJBijC1MYtro1asXhg4dqtl6qampOH36tGbrWTKRz1PZ/b7Pnz8/XF1dSesZUoBA1b17d7i5uZHjGLNniqKQpyD8/fffuHv3rqSMTIM6AcHf3x+KokjKhrGnE56ofxZv376dZbE7y9r58+fx448/onPnzihXrhwKFSqE0qVLY/DgwYiOjjZojfDwcFSsWBHbtm17rQHVlStXMHDgQLz//vuaTTt8/Pgx5s+fLxRbqFAhLkBgjFklkQKE/fv3S8jEcqWnp6Nbt264fv268BpcxM+YadWpU4ccwwUIjGmPCxAYY4wxC0DtrPz48WOcPXtWUjbaOXPmDB48eKDZejNmzHj+/8+ePYtOnTrBz88P3bp1w+jRo4XW5AIE9iqRhwg7dux4/v/j4+PJ3d8AYMiQIShTpgw5zhaNHDkSjo6O5LigoCB8/vnnEjJi9k6n05Hfq48fP27xxYKGPgx+UeXKlSVkYplKlSqFadOmYfv27dizZw9+//13tGnTRmhKS+/evYUe9GTFzc0N3t7emq1nKs7Ozvjyyy+RlpaG3r17C62xevVq3Lx586VfO3ToEA4ePKhFis+JFLYyppWgoCDkzZuXFGMJBQgi03+y6wTPTOu7775D27ZtNVsvKipKs7UsGbUAIWfOnChevHiWryuKAn9/f9Ka2RUgnD59WujP5nvvvUeOYYzRC7ZVVcWuXbskZWMa1AKEokWLSsqEsf+IfJ/LrPmRvVFVFWFhYejfvz/atWuHNm3aYPLkya89i4qIiEBISAiKFSuG/v37Y9WqVYiOjsaNGzcQGxuLGTNmoEqVKvjhhx+yLRx48OABevXqhaSkpGzzWrhwoWaNe1auXImEhASh2EmTJgnds2aMMXMrVaoUcufOTYqxtwkI8+bNM6room/fvvw5lzETq1SpErm4nQsQGNMeFyAwxhhjFoA6AQGwjof5kZGRmq63Y8cO/P333/jss89QtmxZrF692qjON66urjwOkb2mYcOG5Jjt27c///9hYWHkeG9vb4wZM4YcZ6v8/f3Rq1cvUkyOHDmwfPly5MqVS1JWzN5RD0impKTg77//lpSNNmJiYkjXv/HGG8iXL5+kbGybTqfDxIkTNVuvdOnSVt011MHBAb/++itatmxJjk1PT0d4ePhLv6b19AMHBwd06NBB0zUZo9DpdOQpCJcuXcKlS5ckZWQYkU7wXIBgORwdHbFq1Sp8/PHHmqxnDfcsjJWRkUH+56xQoQIcHByyvYZ6aCG7w7/Lli0jrQU8zdEepl4xJgO16zoAbN68WUImppGSkoJr166RYgICAiRlw9h/zF2AcPbsWURERODMmTNITk7WbF2Zbty4gbp166JevXr48ccfsW7dOmzYsAEjRoxA2bJlMWHCBKSlpWHu3LmoV6/ea9/LX/XkyRP873//w6JFi7K8Zvz48YiPjzcov2+++UaoqPJVs2fPFop777330LNnT6P3/z979x0X1Zm+DfwaOgIqRVFEY4MgYAex0eyoiIoNsST2EmONb6IGk9gSS2LcRFdxjWI32LE3BDUgRcWCxl6wYWIXlTLvH/nEX4wI85w5Z2aA6/vXypy77G4iM3PO/dxERPpgZGQEHx8foZhLly7hwYMHCnVkWF6+fInp06dLju7jrEcAACAASURBVHdzc8MPP/wgY0dEpAkbGxvhTeEpKSmybdYior9wAIGIiMgAiJ6qDABpaWkKdCIvuSeI8/Ly4Obmhm+//RavX7/WOl/Pnj1hY2MjQ2dUnPj7+xf6QMi/7d+//82H1djYWOGaY8eOhbW1tXBccTZp0iSYmppqfP3cuXMlDXMRaUrKyf+GfJJGbm4uzp8/LxRTu3ZthbopGfz9/dG8eXNZchWH0/lVKhUWLVok6fff0aNH3/znu3fvYv369XK2htatW8PR0VHWnESimjZtKhyjzUltckhKShK63tTUFLVq1VKoG5LCxMQEP/30E9LT0zFhwgS0b98erVq1wqBBg4RzlYQBhMuXLwtvfdRk6EZ0A8Lt27fx6tWrd36uVqslDSAMHjy4SA86EumTt7e3cMyePXuK7AMQBW1geR+eDEu64OXlBQcHB6GY1NRU3L59W3JNtVqNn3/+GZ6ennB1dYWvry8+/PBDODk5YcaMGbJuipbbvXv3EBAQgCNHjuT7+uvXrzF58mS0aNECI0aMQG5ursa5R48ejYyMjHd+npWVhSVLlmicJzc3F0OGDBGq/W8pKSmSTvT28/PDwoUL+f6IiIo0Kdt5S8oWhKVLl+b7u0oT5cuXx86dO1GmTBmZuyIiTYgeoHHv3j3J/74TUf44gEBERGQAatWqJby6tSjczJd7A4Lcxo0bp+8WyACVKVMGjRo1Eoq5c+cO0tPTAUgbQBg+fLhwTHFXs2ZNfPvttxpd2717d9lOaiV6HymnwBry78ErV67g5cuXQjEeHh4KdVMyqFQqfPnll7Lk6tmzpyx59K1y5cqYOXOmcNw/H4pYsmQJsrOz5WyLpxqSQZAygHDs2DEFOtFMbm6u8ACCp6cnzMzMFOqItOHm5oY5c+Zgx44d2LdvHyIjI4WHMYvCoQnaUmrrh+gAAoB8Tw9OSUnBxYsXhfKYmpoiLCxMuD4R/cXJyUn4c9O1a9fw+++/K9SRsgrawPI+HEAgXTA2Nka7du2E4wo6rb8gr1+/xqBBg/DJJ5+8s23y8ePHmDJlCry8vPDw4UNJ+ZU2depUjQ6piI+PFx4AePLkSb6nQq9fv174f4+kpCTJGwwAYOHChZLitmzZInz/jojI0Ihu2gSA5ORkBToxLC9fvpS8udjCwgLbtm3j+1siPZJy79aQD48jKoo4gEBERGQAzMzMhE9+NPSb+Xl5eZJuxutKy5YteZIzvVerVq2EY44cOYIbN27gypUrQnFNmzaFlZWVcL2SYMyYMYUOZ3Tr1g2rVq3iCVSkuFq1asHc3FwoxpC/xPr3DXFNcABBe61btxYecvu30NBQuLm5ydSR/o0YMUJ489CJEyfw/PlzqNVqrFq1StZ+SpUqhZCQEFlzEknh7e0t/O+GPgcQzp49i+fPnwvFaPIgNhkO0W1j9+/fx927dxXqxjBI+c6jXr16hV4jZQAhv1PIpWw/CAoKgp2dnXAcEf2ftm3bCsfs2bNHgU6UxwEEMmRSNgcuXrxY+AF7tVqNAQMGYNmyZQVed+HCBfTq1Qs5OTnCfSnp9u3bhfaurcjISDx9+vStny1atEhSrkmTJuHOnTvCcU+ePMG6desk1bO1tRWOIyIyNFK+kz558qQCnRiWJUuWSNqApFKpsGrVKvj4+CjQFRFpSskBhGvXrmH+/PkYNGgQOnfujODgYHTs2BHh4eEYP3485s2bh7i4OOHD3oiKGw4gEBERGQjRm/m3bt2S9EWrrly5cgVPnjzRdxvvxe0HVBApAwiJiYk4fPiwcFxAQIBwTElhZGSEhQsXYseOHQgMDHzrhNz69etj9erVWLt2LU/OJZ0wNTUV/l194sQJ5OXlKdSRdjiAoB9ybEGYPHmyTN0YBmNjYwwaNEgoJjc3F4mJiUhLSxM+2bkwnTt3hrW1taw5iaSwtraW9HvnxYsXCnVUsMTEROEY3qQtWkT/eQSKxuZGbYgOIBgbG8PT07PQ6+QYQMjNzZX0kB23HxBpT8qp67t371agE+VJGUCoXr26Ap0Qvatt27YwMhJ7FOL69evYtWuXUMx//vMfrF69WqNr9+7di8jISKH8Stu8ebPsWwX/7cmTJ28NOaSmpuL48eOScj1//hzz5s0TjluzZo3wZyUjIyMMGzZMuBYRkSGyt7eHi4uLUIwhHzQoh+vXryMiIkJS7OzZsxEaGipzR0Qkqn79+sKHFBY2gJCdnY1x48bB1dUVY8eOxf/+9z9s3boVMTEx2LFjB9asWYPvv/8eEyZMgL+/P6pVq4alS5ca7P1gIqVxAIGIiMhA1KlTRzjm4MGDCnQij9TUVH238F4ffvihpJuBVHI0btwYpUqVEopJSEhAbGyscC0OIBSuffv2OHjwIB4+fIhbt27h5cuXSE1NRe/evbn+mnSqQYMGQtc/f/4cv//+u0LdaEd0AEGlUglva6L8dejQAZ06dZIcWxxPDJfyu/DIkSP49ddfZe+lT58+suckkqpp06ZC1+fk5CA5OVmhbgqWkJAgHNO4cWMFOiGlcADhXaKnQbq7u8PCwqLQ66ScDv7vAYRjx44Jn+JoZWWF4OBg4dpE9DZfX19YWloKxcTGxhbJUxNFt4Da2tqiTJkyCnVD9DY7Ozvh99OA2Mn8cXFxGD9+vFD+r7/+Gs+ePRNtSzExMTE6qTN//vw32yWkbj/424oVK4SHJpYsWSJcJyQkBJUrVxaOIyIyVKL3N27cuIE//vhDoW70Kzc3F/3798fjx4+FYz/66CPh3/9EpAwbGxu4uroKxaSkpECtVuf72t9/N/zwww8av9+8e/cuBg8ejODgYDx48ECoF6LigAMIREREBkLKzfz9+/cr0Ik8DHkAYezYscKnH1HJYmZmBm9vb6GY9PR0bNu2TSjGxMRE0o2wkqpUqVKoVKkSzM3N9d0KlVBSVnka6u/Dc+fOCV1frVo14cEsyp9KpUJkZKSkm9hTpkxRoCP9a9asmXCMEgMI5cqVQ+vWrWXNSaQNKe8Tt2/frkAnhRPdgGBlZcXNOkWMlEMT0tLSFOjEMNy5cwf37t0TitF0iNDe3h5WVlZCuf99CvnmzZuF4oG/HrITrUtE77KwsBAesM3KykJ8fLxG1758+RJHjhzBkSNHkJ6e/t4HJ3RBdAOClAErIm1IGTDftWuXRv9sv3jxAmFhYcjJyRHKf+/ePcyfP1+4LyU8f/4chw4d0kmta9euYcuWLXj8+DHWrFmjVa4HDx7gwIEDGl+fkpIi6RTvkSNHCscQERmyevXqCceIDt4XFXPnzpW01d7CwgIzZ84UPnGdiJTj5eUldP29e/eQkZGR72szZ87E2rVrJfWxc+dO1KtXD3FxcZLiiYoqPnlHRERkIBo1aiT8UPz+/fv1epOpIIWtLtOXhg0bYuDAgfpug4oA0RNZ1Wq18FR7o0aN+IAHUREiekIQYJi/D3Nzc3H+/HmhGD4kKq/y5cvj4MGDqFixosYxQUFBxfa08MqVK6NKlSpCMfv27ZN9w8jo0aO5WYcMipQBhOXLl+PVq1cKdPN+T548ER5s8/b2hrGxsUIdkRLs7Ozg7OwsFFOcNyBIeYhN0wEElUqFqlWrCuX+5wYEtVqNLVu2CMUDQFhYmHAMEeVPyubVjRs3Ij09HRs3bsSMGTMwefJkfPXVV4iLi0Nubi7+/PNPfPzxxyhXrhx8fX3h6+sLd3d31K1bF1FRUQr8tygcBxDI0IWHh8PGxkYoRq1WY/HixYVet3DhQuFtQ3+bPXs2MjMzJcXK6eDBgzr97LBgwQLExMTgxYsXWucSGWKIjIwUzu/q6ooWLVoIxxERGTIpm3WL4wDCzZs3ERERISl2+PDhQt/pE5HypBwel98W4aSkJHz99dda9ZKRkYHAwEBMmzbtzfYvouKOAwhEREQGomzZssInrt+6dUv2B6/koFarDfLEZ1tbW6xYsYIPlpFGfHx8FK8heiIeEemXp6cnTE1NhWIM8ffh5cuXhW8wcwBBfjVr1sT+/fvh4OBQ6LWOjo5YtmyZDrrSn+bNm+u1fpUqVTBu3Di99kD0b1WqVIGTk5NQzIMHD7Bp0yaFOspfUlKS8GC8Lt5rk/xENzeeP39e5wMxuqLkAAIArQYQ0tLShB8KtrOzQ5s2bYRiiOj92rZtKxyzePFiuLu7o1u3bpgyZQpmzpyJr7/+Gv7+/jAxMYG9vT2WL1+OZ8+evRV3+vRp9O/fH5988olOH3B4+PAhHj9+LBRTvXp1hbohyp+1tTX69esnHLdy5coC/33KycnB7NmzJff19OlTzJw5U3K8XHbs2KHTenFxcVi6dKksuTZv3oysrKxCr8vMzJS0cWHw4ME83ZqIih0pAwhSPvsautmzZ+P169fCcZaWlpg4caICHRGRNqQMIPz2229v/fnFixfo27evLJ+p8/LyEBERgTZt2uDOnTta5yMydBxAICIiMiCtWrUSjhFZNasrN27cwJ9//qnvNt7i7++PU6dO8QFK0hgHEIjo38zMzFC7dm2hmNTUVOTl5SnUkTRnz54VjuHvT2W4u7sjPj6+wJs/Tk5O+PXXX1GhQgUddqZ7zZo101ttFxcXHDhwAJaWlnrrgSg/KpUK/v7+wnGanNgqJylrqYvrRpfiTnQAIScnR3g7RlEh5SEMkf/9RAcQ7ty5g5cvXwL464E8UZ07d4aZmZlwHBHlz9XVVfjfY239/PPPGDNmjM625V65ckU4hhsQSB+GDx8uHHP79m0cOnTova8nJiZqvcFg8eLFePTokVY5tKFWq3U+gAAAsbGxsuR59uyZRv2PGjUKT58+FcptamqK/v37S22NiMhglS9fXvigi+I2gJCdnY2VK1dKih0+fHix/46eqCiqX7++8ODokSNH3vrzpEmTcOHCBTnbwsGDB1GvXj3s3btX1rxEhoYDCERERAZEygDC/v37FehE3B9//IEDBw7gyJEj2Lp1q157cXNzQ0hICIYNG4YFCxbgwoULOHToECpXrqzXvqhocXJyUvSfGRMTEzRt2lSx/ESkDNGTNJ48eWJwa4o5gGBY3NzckJCQgBkzZqBJkyYwNTWFmZkZPD09MXHiRJw/fx6+vr76blNxutiAEBwcDGtr6zd/rlixIiZNmoTffvsNNWvWVLw+kRTdunUTjjl8+DDOnz+vQDfvioyMxP/+9z/hOG5AKJrq1KkjHFPcHlb4m+h/r2rVqqFs2bJC14u6ceMGAGDLli3CsV26dBGOIaL3U6lUaNeunc7r/vTTT/jxxx91Ukt00wrAAQTSDw8PD/j5+QnHrVq16r2v7d69W5uWAABZWVnYsGGD1nmkSktLw61bt/RWXw6jRo0q8PXNmzdj/fr1wnk7d+6McuXKSW2LiMig1atXT+j68+fP48WLFwp1o3vx8fHCW7wAwMHBAZMnT1agIyLSlo2NDdzc3IRikpKS3mzTunbtGn7++WclWsP9+/fRvn17SZ+fiYoKDiAQEREZkCZNmgifvHrw4EGdrtf+tzNnziAwMBDly5dHq1at4Ovri9GjRwvn+emnn7RaaVumTBnMmTMHr169Qnp6OrZs2YJFixZh1KhRcHV15bpckkTJB6MCAgJgZWWlWH4iUkaDBg2EY7Zt26ZAJ9KJDiAYGRkJf3lHYszMzDBp0iQcO3YMT58+xfPnz3H69Gl89913sLGx0Xd7OuHh4YEyZcoolr9bt27Ytm0b7t+/j+vXr+PevXu4ceMGZsyYAXt7e8XqEmkrODhY0ulqS5YsUaCb/5Obm4uxY8diyJAhwrGVK1dGxYoVFeiKlCa6AQF4d6V5cfD48WPhk78L2naUHyknp1+9ehVXr17FqVOnhOKsra0lHYhBRAXT12DPuHHjdHI4DAcQqCgZMWKEcMyKFSvw7NmzfF+TYwDh7xr6oo/tB3K7e/cuZs2ale9rDx8+lPT/OwAMHjxYm7aIiAya6GfTvLw8nDlzRqFudE/qvZrIyEjY2dnJ3A0RyUX0gKvs7GwkJSUBABYsWICcnBwl2gIATJ48mZ+FqVjjAAIREZEBMTc3Fz7h9vHjx0hJSVGoo4KtX78eDRs2RGxsLPLy8iTnMTIywkcffSTpi90KFSrgu+++w/Xr1zFhwgSYmZlJ7oPo3xo3bqxY7vDwcMVyE5FyRDcgAEV/AKF69erCA5Iknbm5OUxMTPTdhs4ZGxujSZMmiuXv3r07AMDS0hJVqlRB+fLlS+T/zlT0mJqaYsCAAcJxK1euRHZ2tgIdAS9evEBoaCjmz58vKV7J99ikLBcXF1hYWAjFxMfHK9SN/og+4A/oZgDh2rVrkrYfBAUFCf//SkSFa9GihaQhQm2p1Wp8/PHHuHv3rqJ1pAwgfPDBBwp0QlS4Ll26SBo8t7Gxeec7nfv37yM5OVmWvo4dO4aLFy/KkkvUrl279FJXbpMmTcKyZcve+fm0adMk/T1YrVo1tGzZUo7WiIgMkuhnU6D4bDZUq9WS7tX0798fnTt3VqAjIpKLlA3bR44cgVqtRnR0tAId/cXf3x8RERGK5ScyBBxAICIiMjBSTp07cOCAAp0ULD09HQMGDMDr16+1zuXm5gYrKyssXLiw0LW5/zRw4EBcuHABEydOVPTEXCq5lNqAYGlpidDQUEVyE5Gy6tWrJ/w758SJE7h586akeufOncPBgwdx5MgRZGZmSsrxT69fv8aFCxeEYjw8PLSuS6SJgIAARfJaWFigffv2iuQm0oXBgwcLb3R78OAB9u/fL3sv2dnZ6Natm1YnKyu5ZYyUZWxsLLwN6sKFC7K8hzEkUh6+0NUAwubNm4Xj+CAFkTJMTEzQu3dvvdR++PAh5syZo2gN0U0wlSpV4rAT6Y2ZmRl69eolKbZz58745Zdf3vx53759crUFAIiKipI1nyZevXqF48eP67yuUoYOHYrDhw+/+XNGRgYWLlwoKdfgwYNhZMRHaIio+CrJAwhnz56VNET7n//8R4FuiEhOooe8An8dmpKUlCT5/m1hHBwcsHr1ahgbGyuSn8hQ8NMTERGRgZEygCDXiTuaUqvVGDp0KF68eCFLvr8fNjM2NsaCBQtw+PBh9O/f/70PeHp6emLHjh1YunQpSpcuLUsPRPlp0KCBIh8Ku3TpAhsbG9nzEpHyTE1NERQUJBwXExMjdP2aNWtQv359eHh4oGXLlvD19YWzszP69++PS5cuCdf/26lTp4SHBzmAQLqi1HBehw4dYG1trUhuIl2oWrUq2rZtKxy3Zs0a2Xv54YcftD4tlRsQijYpN/SOHj2qQCf6I+Xhi3r16gldb2dnJ/yZ8fjx4zhy5IhQjKmpKTp06CAUQ0Sa69u3r95qr1+/Hmq1WrH8ly9fFrq+WrVqCnVCpJk+ffpIilOr1Rg4cOCbwYPdu3fL2RaioqK02i4tRWpqqiwHSxmKnJwcdOvWDdeuXQMAzJo1C69evRLOY29vj5EjR8rcHRGRYalWrZrwvXVdP4egFCnbDwYNGsT7uURFQNWqVeHk5CQUc+zYMWzYsEGhjv56n1+pUiXF8hMZCg4gEBERGZi6desKrwM+d+6cQt3kb+/evYiPj5ctX/fu3d/6s5+fH5YvX46HDx/i8uXL2LhxIxYtWoRFixYhMTERp06d4im2pBOlSpVC3bp1Zc+rzxvQRKS94OBg4RhNv9xWq9WIiIhAeHg4Tp48+dZrr1+/RlRUFJo1a4YzZ84I9wAACQkJwjEcQCBdqVmzJpo0aSJ73sGDB8uek0jXhg4dKhyzefNm2YbGASArKwvfffedVjlKly6Nhg0bytQR6YOUleZyfn9gCEQHEMqVKyd8E1SlUglvQTh48KDww8aBgYHcKEmkoLp168LT01MvtTMyMt48jCu3J0+eCA/GcwCB9M3Hxwc1a9aUFKtWqxEeHo6MjAzs2bNH1r5u3Ljx1un9unDs2DGd1tOFBw8eICQkBDExMfj5558l5Zg/fz4PvCKiYk+lUgkPyKekpOD06dMKdaQ7UgYQunbtqkAnRCQ3lUol/J3lkydPMG/ePEX6+eyzzyQdZkdUFHEAgYiIyMAYGRnB399fKObixYs6O7FGrVZjypQpsuWrVKnSe09QVKlUqF69Orp27Yphw4Zh2LBhaNSoEVfgkk75+PjIms/R0VHSphMiMhxBQUHC21EOHjyIp0+fFnrdjBkzMG3atAKvuX//PgIDAyWtBU1MTBSO4YOipEv9+vWTNZ+HhwfatGkja04ifejYsSMqVKggFPP8+XNs375dth62bt2KP//8U6scvXr1goWFhUwdkT40bdpUOEb0VH5D9urVK+FDIOrXrw+VSiVcS3QAQYouXbooXoOoJFOpVHo9hELKALomUlNThWNq1KihQCdEmlOpVJK3IABAZmYm3NzckJmZKWNXf1Fic1lBfvvtN9lymZiYGMz7+7S0NEmHhgB/bU4MDw+XuSMiIsNUv3594ZiffvpJgU505+7du8L3RqysrBAYGKhQR0QkNylbW0UZGRnhhx9+gLm5+Xuvady4MWbMmKF4L0SGgk/vERERGaDatWsLXZ+bm4uLFy8q1M3btm7dKuuqxZ49ewo/xEmkSwEBAbLm6927N0xMTGTNSUS6ZWtrCz8/P6GY169fY9OmTQVes3fvXkRERGiU78GDB+jfvz/y8vKE+hB9AMXW1hYuLi5CMUTa6NGjB8zMzGTLN3bsWEkPfRIZGhMTE/Tq1Us4bu3atbL1EBUVpVW8o6Mjpk+fLlM3pC92dnbCp3mnpqbi+fPnCnWkW2fPnkVOTo5QjJSHOwCgVq1akuJEdOrUSfEaRCVd79699fZ+VKkBhKSkJOEYqX8XEslJ2wfMnz17JlMnb9u7d68iefOjVqtl24BgZGSENWvWYOrUqbLk05fSpUvjv//9L787IKISQ8qBQ6tWrcLDhw8V6EY3pGwwatu2rcEM2RFR4aRsbRXVu3dvjBkzBsePH4ebm9s7r5ctWxZr166Fqamp4r0QGQoOIBARERkgd3d34RjRE/ikyM3NxZdffilrzrCwMFnzEcktJCQEDg4OsuUbMGCAbLmISH+kPKw1depUvHz5Mt/Xrl+/jrCwMKjVao3zHTp0CPPnz9f4+gcPHuDy5csaXw+Am4dI5+zs7NCxY0dZcpUrV44nGFKx0rt3b+GYnTt3ynKD+O7du5Ju1v7N2dkZu3btQrly5bTuhfRP9IZeTk4Ojh8/rlA3unXixAnhGKkP3Sp947Rx48ZwcnJStAYR/fU7sGvXrnqprdQAgpTDaby8vBTohEhMzZo10bhxY3238Y4bN27g9u3bOqt1584drfPUr18fMTEx6N69O1q2bClDZ/ozffp0ODs767sNIiKdad26tXDMixcv8MsvvyjQjW5Ief/KgX2ioqV27dqwsbFRtEZoaCgAoE6dOkhKSkL//v3fen3ZsmU62WhKZEj4FAEREZEBMtQBhPXr1+PMmTOy5XNxcZF0ygKRLpmbm+OLL76QJVefPn2ETwslIsMkZaX79evX8dNPP+HmzZtYsmQJvvnmG0RERCAiIgJVq1bFn3/+KZzziy++wOnTpzW6VnTFMACDvDFPxV+/fv1kyTNy5EieUkXFipeXF2rWrCkUk52djc2bN2tde82aNcJbd/4WFhaGlJQUnnxcjEh5MH7//v0KdKJ7uhxA8PX1VXQQtEuXLorlJqK3zZkzB+bm5jqve+LEifcOwWtD9AEuJycnDjyRwRg9erS+W8jXb7/9ppM6UrYfTJs2DbGxsViwYAFWrlyJI0eOICUlBUFBQQCABg0aoGzZsrL2OW/ePFhaWsqaMz9VqlTBkCFDFK9DRGRIKlSogLZt2wrH/fzzz8jNzVWgI+WJPt9gZGSEDh06KNQNESnB2NgYTZs2VSy/lZXVW393WltbY/ny5Vi+fDlKlSqFTz75hN+1UYnEAQQiIiID5OLiAmNjY6EYpQcQsrOzZV+lGxYWxrW2VCSMHTsWQ4cO1SpH7dq1MW/ePJk6IiJ9q1GjBjw8PITjPvvsM1SpUgVDhw7F1KlTMW3aNEybNk1yH69fv8Ynn3yi0eYEDiBQUREUFAR7e3utcpibm2P48OEydURkGFQqlaQtCAcOHNC6dlRUlKS4Xbt2Yc2aNShfvrzWPZDh8PX1FY6JjIwssg8q/JPoAIKVlZXw4NDfypYtq+jgTufOnRXLTURvq1atGqKiogodjq1YsaKsmwKys7MlDU4V5M8//8SVK1eEYrj9gAxJjx490KhRI3238Q5dDSBIqePn5wd/f3+MGjUKffr0QbNmzd66r2NsbIyAgADZemzYsCHGjh2LTZs2wdTUVLa8+ZkyZYpeBsSIiPRt1KhRwjFXrlxBdHS0At0o7+zZs0LXe3t7w8HBQaFuiEgpUr6z1FSHDh3yHZDt378/Tpw4gTlz5ihWm8iQcQCBiIjIAJmbmwvfoBb94CxqxYoVuHTpkqw5w8LCZM1HpBSVSoVFixZh/fr1qFatmnBsz549ERsbyweviIqZkJAQfbcAAIiLi8Phw4cLvS4hIUE4tyHelKfiz8zMDIMGDdIqx8CBA/l7l4olKZ+hkpKStKqZlpaGU6dOCcft3r0b7dq106o2GaYqVaqgcuXKQjGZmZlYtmyZQh3pRm5urvC/C3Xr1tVqi0FgYKDk2IK4u7vD1dVVkdxElL8ePXrgyJEj8PPze+dAliZNmiA6OhoZGRlISkpCWloaGjRoIEtdKZ8DC5KSkiIcwwEEMiRGRkbYunUr6tSpo+9W3iJlM4Eu6piYmGj077Ccg41fffUVVCoV2rVrh//+97+y5f23atWq4aOPPlIsPxGRIQsKCkKNGjWE46ZPny55Q6a+3L9/H5mZmUIx9erVU6gbIlJSx44dFcsdGhr63tdcXV25jZtKLA4gEBERGSh3d3eh63///XdkZ2cr0surV6/wzTffyJozJCQEbm5usuYkUpJKpUKPHj3w+++/Y8+eTOAODQAAIABJREFUPfjoo4/eOZ25Vq1aGDJkCEaNGoUJEyZg0aJFSE9Px7p162BnZ6enzolIKQMHDjSYTT5Lly4t8PW8vDzhDQiurq78u4v05rPPPkOZMmUkxZqZmeHzzz+XuSMiw+Dm5ib8QOLFixfx6NEjyTW/++474ZgGDRq8tZKaip/mzZsLx0yaNEmrfxb17fLly3j+/LlQjLYbDOQ8TfifuP2ASD8aNmyIw4cP49atW9i+fTv27duHc+fO4dixYwgNDX3z+bJ27dpISUnBpUuXsHTpUixfvhzbt29HfHy8cE25BxCSk5OFYziAQIamQoUKiIuLk33jszZSUlLw6tUrRWs8f/4cJ0+eFIqpV68eSpUqVeh1YWFhqFSpktTW3ujUqdNbD44NGDAAn376qdZ58xMREaH4hgUiIkNlZGSEkSNHCsedOXMGW7duVaAj5Ug5xFHK9msi0r+6desqcpiHhYUF2rdvL3teouKAAwhEREQGSvSDbXZ2Ni5fvqxIL0uWLMHNmzdly2dmZobZs2fLlo9Il0xMTNCmTRv88ssvyMzMxI0bN3Du3Dk8efIE586dw+LFi7FgwQLMmTMHw4YNw4cffqjvlolIIdWrVzeYk9Kio6Px8OHD975+4cIFPHnyRCinj4+Ptm0RSWZvb48vvvhCUuygQYOET+YmKkqCg4OFY/J7WPDZs2dYv349+vXrB39/f3Tt2hXjxo3D9u3b8fjxYwB/PQi1Zs0a4XpDhgwRjqGiRcpK8wcPHmD48OF49uyZAh0p78SJE8Ix2g4g+Pr6arVB4X26dOkie04i0pyTkxM6duyIVq1aoVatWu+9rkaNGhg4cCD69++Pjh07olmzZnBwcBCqJfcAgpTNSg0bNpS1ByI5lClTBl999RWysrLg7Oys73bw+vVrSe81RCQnJyM3N1copkmTJhpdZ2ZmhgULFmj1vsXS0hI//vjjOz+fN2+e7A+SNW3aFH379pU1JxFRUfPxxx9rNGT2b9OmTYNarVagI2WcOXNGOMbT01OBTohIF2bNmiXp77aC9O3bF9bW1rLmJCouOIBARERkoEQ3IADAuXPnZO8jLy8P33//vWz57OzssG/fPri6usqWk0hfVCoVKleujFq1asHGxkbf7RCRHkybNg2Wlpb6bgOvXr0q8AFRKQ+dNG7cWJuWiLT26aefCg8ScPsBlQTe3t7CMcePH3/zn58+fYpvv/0WVatWRa9evbBy5UrExcVh8+bN+OGHH9CpUyfY29sjNDRU0unrZmZm6NGjh3AcFS3BwcGSHjBbt24dbGxsEBwcjJiYGAU6U05cXJxwjLYDCKVLl5b9wV1nZ2c+DExURKlUKuHPaTdu3MDt27dl60F0A0KVKlVQvnx52eoTyc3CwgLnz5+XfQPkwIEDhWN+++03WXv4NynvZZo2barxtV27dsWaNWtgYmIiXAcAvvzyS1StWvWdn5uYmGDlypUoW7aspLz/VrVqVaxcuRLGxsay5CMiKqrKli2Lfv36CcedOHECO3bsUKAjZXADAlHJ4uPjg507d8o2hFCmTBnJh2URlQQcQCAiIjJQhjKAkJCQgGvXrkmK/efJ746Ojhg3bhySk5Ph5+cnU3dERET6ValSJYwbN07fbQAAIiMj33vyUGJionA+bkAgfbO0tMT06dOFYrj9gEoCKQMIf59WHBsbCxcXF3zxxRf4448/3nt9bm4uNm3aJOmk+k6dOsHW1lY4jooWZ2dndOzYUXJ8TEwMgoOD8dVXXxWJkxPVajV27dolFGNiYiLLQwtyn/g7cOBAqFQqWXMSke5IGRSX8nkwP/fu3RPekuvl5SVLbSIlWVlZYcCAAbLlq1q1KiZOnCgcd+zYMdl6yM+hQ4eEYzTdgPC3nj17IjExUfj+VqNGjTB+/Pj3vl6pUiUsXLhQKGd+2rRpg4SEBFSvXl3rXERExcHYsWMlHS4gx9/JuiK6AcHBwYEDtERFnL+/P5KTk7X+PGplZYXNmzejWrVqMnVGVPxwAIGIiMhAubq6Cn/gV2IAITo6WjjG1NQUV69eRXp6OrKysvDy5Uvcvn0b8+bN45tzIiIqdiZOnIhy5crpuw2cOnUKqamp+b4mugHBwsICderUkaMtIq306dNH4xMXy5UrhylTpijcEZH+lS9fHlWqVBGKSUpKwn//+1+0bNkS9+7dU6izv0g5PY+KpunTp0t6UOGfvv76a0yYMEGmjpRz8eJFXL16VSjG3d0d5ubmWteWsonkfRwcHAxmeJaIpJEygCDXQ81STrqVMjhJpA/Dhg2TLVffvn3h4uICBwcHoTglNyC8fPlS+O+CihUrCn/uAIAGDRogOTkZEydOhJWVVaHXV69eHVu3boWZmVmB1/Xq1UvypjVbW1usWLECu3fvhqOjo6QcRETFkaurK3r16iUct3fvXjx58kSBjuSlVquFNyB4enoq1A0R6VKtWrVw7NgxfP311zA1NRWO79ixI86cOSP7wSBExQ0HEIiIiAyUpaWl8CksUlYIFkStVksaQBg8eDCqVq0KlUoFCwsLmJuba/1QAhERkaEqXbo0PvvsM323AQBYunTpOz979uwZTp8+LZSnYcOGkr6QI5KbkZERoqOj4eTkVOB1KpUKq1atQsWKFXXUGZF+iT7Ml5GRgeHDhyMvL0+hjv7i6uqK9u3bK1qDDEft2rUxfPhwrfN8//33WLVqVaHXZWdnIzc3V+t6UuzevVs4Rq4blM2bN4eJiYnWeaysrLBixQqULl1ahq6ISF+8vb2Ft5jIMYCgVqsxf/584ThuQKCiokaNGmjXrp0sufr27QuVSiW8PSAjI0N4y4imEhIS8OrVK6EYf39/yVuTLC0t8d133+HmzZuYM2cOWrZsCUtLy7eusbKywrhx43Dy5ElUqFCh0JwqlQqLFy9Gw4YNNe7Dzc0NP/zwAy5duoR+/fpxCxQRUT4mT54s/Pdjbm4ujh49qlBH8rlz5w4ePXokFCPHJkMiMgympqaIiIjAqVOnNPqeTqVSoWvXrkhJScH27dtRtWpV5ZskKuL4JCAREZEBE11Te+HCBeTk5MhWPykpSfgLbwsLC0yePFm2HoiIiIqCAQMGwMLCQt9tYN26dXj58uVbP0tJSRF+4FTKqZpESqlYsSIOHTqE1q1b5/t6zZo1sW/fPrRp00bHnRHpj6GeJjxr1iwYGxvruw3SoW+++QZ2dnZa5xk8eHC+m5zS09MRHh6OSpUqwdzcHPb29ujQoQOWLl2KrKwsretqateuXcIxQUFBstS2sbFBSEiIVjnc3d0RHx/PASGiYqB06dLCD0UlJye/8zlR1MGDB4UH2wEIPShMpG8jRozQOke7du3g4uICAMIDCABw+PBhrXvIz8GDB4Vj5BimtLW1xYQJE7B//348evQIZ8+eRWxsLI4fP45Hjx5h3rx5sLGx0Thf2bJlsW/fPvj5+b33mgYNGuCnn35CRkYGzp07hzFjxsjyfpWIqLhyd3dHt27dhOMOHTqkQDfyOnPmjHAMNyAQFT+1atXCgQMHsG7dunw/o/r4+GDOnDm4du0aNm7ciAYNGuihS6KiiQMIREREBkx0AOHVq1e4cuWKbPWlbD8ICwsr9IRaIiKi4sbe3h5hYWH6bgOPHj1CTEzMWz9LSEgQzuPj4yNXS0SycHV1xZ49e3Dw4EHMnDkT/fr1w+eff47o6GikpaWhZcuW+m6RSKcMcQChSZMm6NKli77bIB2zs7PDN998o3Wely9fomvXrnj48OGbny1YsAAeHh5Ys2YNbt++DbVajcePH2Pnzp0YPHgwKleujIiICDx9+lTr+gXJysoS3oBgYWFR4INxoj777DPJwz2JiYk4c+YM6tevL1s/RKRfog81v379GikpKVrV/OGHH4Rj3N3dYWtrq1VdIl1q37698FbqfzI2Nsa8efPe/FnKAMLOnTsl1y+IlIdE5drm9DczMzO4u7vD398f3t7ekjc82draIjY2FrGxsejbty9at26NJk2aYMKECTh8+DCSk5MxcuRIODk5ceMBEZGGpkyZIhwTGxsrfyMyO3v2rHAMNyAQFU8qlQo9e/ZEUlISLl26hAMHDiAuLg5//vknEhISMGHCBFSpUkXfbRIVORxAICIiMmBSPuCmpaXJUlutVksaQOjevbss9YmIiIqakSNH6rsFAEBUVNRbf05MTBTOwQ0IZIhUKhUCAwPxxRdfYMWKFZg1axZCQ0NhaWmp79aIdM4QTxOePXs2H/ApoYYOHSrLyWDXr1/HZ599BgBYsWIFRo8eDbVa/d7r//jjD0ybNg1ubm44fvy41vXzk5OTg/DwcOG4gIAAWX8/+fj44PvvvxeKUalUOHr0KBo1asR/N4mKmWbNmgnHHD16VHK933//HTt27BCOk3KSLpE+GRsbY/bs2ZLjZ82a9dahUt7e3sIDhLt27ZJ1yzUAvHjxQvi7IWdnZ9SsWVPWPuSkUqng7++PqKgo7N27F8eOHcOcOXPg5+fH9z1ERBLUqVPnzQYfTaWkpODx48cKdSQPKRsQOIBAVLypVCrUqFEDLVq0gK+vL4fmibTEAQQiIiIDJroBAQBOnTolS+0TJ07g6tWrQjFlypTh6bNERFRiNWzYULbNAeXKlcPnn3+OX3/9VTh2165duH//PoC/BgpFNyBUrFgRzs7OwnWJiEh3ypQpgw8//FDfbbzRqVMnNG/eXN9tkJ6YmJjgl19+gampqda5/ve//yEyMhLDhw/XOOb27dto06YNTp8+rXX9vz19+hTTp0+Hk5MTNm/eLBwfFBQkWy9/+/TTT7Ft2zaUL19eo+t//PFHNG3aVPY+iEj/dD2AIOWBbBMTEwwdOlRyTSJ96dq1K8aNGycc179/f0yYMOGtn1lZWQkPaT569AjHjh0Trl+Qo0ePIjs7WygmMDCQD/ITEZUwoptv8vLycOTIEYW6kYfoBoSKFSvCzs5OoW6IiIiKHw4gEBERGTB3d3fhE3LkGkBYt26dcExISAjMzMxkqU9ERFQUffLJJ8IxtWvXxtmzZ3Hq1CkcP34c165dQ0ZGBmbNmoWuXbuicuXKQvlycnLe/B6/desW7ty5IxTfuHFj3mQmIioCvL299d3CG6NHj9Z3C6RnderUwZQpU2TJNWTIEGRlZQnFPH78GKGhoXjy5InW9S9evAgPDw98+eWXyMzMlJSjXbt2WveRn+DgYFy+fBlff/01rK2t873G3Nwcc+bMwahRoxTpgYj0r0aNGhoPI/3t2LFjBW6VeZ/Lly9j+fLlwnE9e/aEk5OTcByRvqlUKsydOxdz5szR+N5Ms2bNsHjx4ny/S+nQoYNwDzExMcIxBTl06JBwjOhDqEREVPQFBAQIx0j5HaMreXl5wgMI3H5AREQkhgMIREREBszCwkL4VEs5BhBev34t6cYS12oTEVFJ16tXLzRq1Ejj60NDQ5GQkAB3d3fUqVMH3t7e+OCDD96cIGxkZIS+ffsK97Fy5UoAEN5+APw1gEBERIbPUAYQPvjgA0k3qan4+eKLL1C3bl291b948SIGDhwo6QHbvz18+BABAQG4efOm5BzVq1eHi4uL5PjCWFtbIyIiAteuXcPSpUvRoUMHNGrUCG3btsXnn3+Oq1evvnMCMxEVLyqVSngLwoMHD3Dx4kXhWtOmTUNubq5w3NixY4VjiAyFSqXChAkTEBcXV+ihEC1atMC2bdtgbm6e7+uGMIBw8OBB4RgOIBARlTxSvtuJjY2VvQ+5pKen49mzZ0IxHEAgIiISwwEEIiIiAyd68/7GjRt4+PChVjW3bdsmfMqfjY0NWrdurVVdIiKios7ExAS//PILLCwsCr128uTJ2LBhA0qVKlXgdVIGEJKTk3H69GlJAwg+Pj7CMUREpHuGMoDQv39/GBnxa2YCTE1NsWbNGtja2uqth+joaCxYsEBy/KxZs3D79m2temjXrp1OtknZ29tj4MCBiImJQWJiInbv3o1Zs2ahYsWKitcmIv1r2rSpcMzRo0eFrv/999/fDLeL8PX1RcOGDYXjiAxN06ZNkZaWhq+++uqd9zeOjo744osvsHv3btjZ2b03R4MGDVChQgWhuunp6bhy5Yqknv/t6dOnSE5OFoqpWrUqqlatKkt9IiIqOipWrCh8MOKJEyfw6NEjhTrSjuh7XwDw9PRUoBMiIqLii3eGiIiIDJyU0wPT0tK0qrlkyRLhmODgYI0etiQiIiru3N3dkZyc/N7f4dWqVcPmzZsxffp0jR7YdHNzk/SQ6dy5c5GYmCgUY2RkBC8vL+FaRESke15eXihXrpy+20C/fv303QIZEHd3d+zYsUOv/2xOnDgR58+fF4579uwZFi5cqHX9du3aaZ2DiKgwohsQAPGHsL755hvk5eUJ1xkzZoxwDJGhKlu2LKZOnYrr168jOTkZe/bswdGjR5GRkYGZM2e+2WD5PkZGRpK2IOzYsUNqy2+Jj48X3mLSokULWWoTEVHRI7oBJy8vD3FxcQp1ox0pAwj16tVToBMiIqLiiwMIREREBk7KAMKpU6ck17t69Sr27dsnHNe9e3fJNYmIiIobDw8PJCYmYsuWLRg/fjxCQkIwevRoLF26FOnp6ejcubNQPikPd0ZFRQl/yV67dm1YWVkJ1yIiIt0zNTVFnz599NqDn58fatSoodceyPA0adIEly5dwuTJk9GgQQOd13/9+jVq1aqFO3fuCMVt3rwZz58/16q2o6Mjt0MSkU40aNAA5ubmQjGxsbFQq9UaXXvnzh2sW7dOuK8aNWogJCREOI7I0NnY2KBhw4Zo06YNmjZtCmNjY41jO3bsKFxv69atwjH5OXTokHCM6MOnRERUfAQEBAjH7Ny5U/5GZCB6b6RUqVKSnssgIiIqyTiAQEREZOB0PYCwdOlS4RhHR0dJp/gQEREVZ+bm5ggJCcHcuXOxZcsWzJ8/HwMHDhR+SAQAevXqBRMTEwW6fFvjxo0Vr0FERPL57LPPUKpUKb3V//jjj/VWmwxb6dKlMX36dKSkpCAnJwf+/v4678HJyQlt2rTBlStXNLo+KipK65pjx47ldkgi0glzc3PhTXmXL19GbGysRtdu3LhR+NR0AIiIiBB6MJuoJGjVqhXMzMyEYg4dOoS7d+9qXZsDCEREJELKZ/dNmzYhJycHAKBWq7Fhwwb06tULH3zwAVxdXREYGIjly5fj1atXcrf7Xvfu3cPly5eFYnx8fArdbERERERv4wACERGRgatQoQLKlSsnFHPy5ElJta5fv44FCxYIx3300Uf8QE5ERKQgBwcHnQz7+fj4KF6DiIjkU7FiRUyYMEEvtatWrYoePXropTYVLcbGxliyZImkIUxt7du3D56enoWefHjr1i0cOHBAq1pdunTB+PHjtcpBRCSiWbNmwjGafve7f/9+4dwffvghevfuLRxHVNxZW1sLnyidl5eHDRs2aFX34cOHOHHihFCMi4sLKlWqpFVdIiIquipUqIBatWoJxWRmZiI+Ph6ZmZlo3749evbsifXr1+PGjRu4ePEiYmNj8fHHH8PT0xNXr15VqPO3iW4/AKS9tyYiIirpOIBARERk4FQqlfAWhLNnz745aUBTarUagwcPxrNnz4TiAGDQoEHCMURERCRm4MCBitfgl+xEREXPhAkT4OjoKEsukROLFy5cqNftC1S0uLq64quvvtJL7aysLPTs2RMPHjx47zWrV6+GWq2WXKNPnz5Yu3atTjZWERH9Tcrnt23btuH69esA/nrAOSUlBfPmzUPfvn0RHByMgIAAdO3aFVu3bhXOPXXqVP49SPQeHTt2FI5Zs2aNVjXj4uKQl5cnFNOiRQutahIRUdHXsmVL4Zi1a9eiR48e2L1793uvuXTpEpo3b66TIQQOIBAREekGBxCIiIiKANEBhFevXuHChQtCMcuWLcO+ffuEYoC/vpCuWbOmcBwRERGJ6dChA9zd3RXL7+bmBhcXF8XyExGRMmxsbGR5sPvnn3/GiRMnEBgYWOB1ZmZmmD9/PoKCgrSuSSXL+PHj4eXlpZfaGRkZmDx5cr6vqdVqREVFScprbm6OJUuWICoqSi8bHoioZAsMDISFhYVQTF5eHhYuXIiMjAy0adMGXl5emDBhAlatWoWYmBgcPnwYmzdvFu7Fw8ODm5GICtCpUyfhmMTERFy6dElyzUOHDgnHFPZZgIiIir/Q0FDhmMjISMTGxhZ63e3bt9G9e3e8fPlSQmeaEx1AUKlUaNy4sULdEBERFV8cQCAiIioCRAcQAODUqVMaX3v37l2MGzdOuAYADB48WFIcERERiTEyMsL/+3//T7H8vXv3hkqlUiw/EREpZ9CgQVo92O3i4oLBgwejdu3aOHjwINLT0zFx4kR4enrC1NQUAGBvb49OnTrh+PHjGD16tFytUwliamqKzZs3o3Llynqpv2zZMly7du2dn6empuLcuXPC+T788EMkJSVh8ODBfA9FRHphbW0t6aH/2bNnw8XFBQcOHJCtl48++khokxJRSfPBBx9IOll57dq1kmtKGUAICAiQXI+IiIoHX19flC9fXrH8KSkpGDNmjGL5s7KykJqaKhTj4eGBsmXLKtQRERFR8cUBBCIioiKgXr16wjEiAwiTJk3CkydPhGvY29ujS5cuwnFEREQkTVhYGKpUqaJYbiIiKppMTEzw66+/wt7eXjjWzs4OK1aseDNoAPy1Fee7777D6dOn8fz5c9y8eROZmZnYunWrpAF5or85OzsjNTVV0omK2srJycGMGTPe+fnq1auFc5mbm+P8+fOoXbu2HK0REUk2atQoSXFZWVmy9tG6dWtZ8xEVR7179xaOWb16NdRqtXDcgwcPkJaWJhTj7u4OR0dH4VpERFS8GBsbo2vXrorWWLx4MVauXKlI7qSkJGRnZwvFSBkSJCIiIg4gEBERFQlubm4wMzMTijl27JhG1yUlJeGXX36R0hZGjBgBc3NzSbFEREQkztTUFBMmTJA9r7e3N2rWrCl7XiIi0p2qVasiNTUVLVq00DimUaNGSEhIQJMmTd57jampKZydnXnCO8nGwcEB0dHRiIuLg4+Pj8ZxJiYmWtdevnw5rly58ubPubm5WL9+vXCehQsXat0LEZEcvLy80LhxY732UK5cOQ5kEWmge/fuwptCLly4gJMnTwrXOnz4sHBMYGCgcAwRERVP3bp1U7zG+PHj8fTpU9nzbtu2TTiGAwhERETScACBiIioCDA1NYW7u7tQTEJCQqEf2tVqNUaPHi2pJycnJ4wfP15SLBEREUk3cOBAODg4yJqT2w+IiIqHKlWqYP/+/Thz5gxmzZqF3r17o2HDhqhUqRKcnZ1Rt25d9OrVCzNmzMC5c+eQmJgIFxcXfbdNJZSvry+OHTuGRYsWwdrausBry5Ytiz179uDu3bsoW7as5Jo5OTmYPn36mz/Hx8fj9u3bQjksLCx08jAGEZGmpG5BkEvLli1hZMRbzkSFKVeuHNq0aSMcFxMTIxxz9OhR4RiRQWYiIire/P39Zb8H8W+ZmZlYsGCBrDmzsrKwbNky4TgOIBAREUnDb4OIiIiKiHr16gldn5OTg9jY2AKvWbt2LX777TdJ/SxevBhlypSRFEtERETSlSpVCl9++aVs+VQqFXr27ClbPiIi0i+VSgUPDw98/vnnWL16NZKTk3Hr1i3cvHkTJ0+exNq1azFp0iTUqlVL360SwcjICMOGDcPZs2cRHh6e75aD0NBQpKWloUWLFnB0dMTMmTO1qhkVFYWMjAwAf30vIqpLly4oXbq0Vj0QEcmpW7duqFChgt7qt2rVSm+1iYqa8PBw4Zjdu3cLxxw/flzoepVKBX9/f+E6RERUPJmYmKBLly6K11mxYgXUarVs+davX4+HDx8KxVSoUAHVqlWTrQciIqKShAMIRERERYSUL3/37dv33tfy8vLw1VdfSeqlT58+6Nixo6RYIiIi0t6IESPg6+srS66AgAA4OTnJkouIiIhIiipVqmDVqlW4evUq1q5di++//x6rVq3ClStXEB0djcqVK7+5dujQoejevbvkWrm5uVi1ahVev36N6Oho4fg+ffpIrk1EpAQzMzMMGTJEb/U5gECkuZCQEFhaWgrFJCQkCD1MmZ2djdTUVKEatWvXhr29vVAMEREVb9p87tbUxYsXcebMGdnyLVy4UDjG398fKpVKth6IiIhKEg4gEBERFRFSbuQUNIAQGxuLixcvCud0dHTEjz/+KBxHRERE8jExMcHq1atha2urdS59PqhCRERE9E/Ozs7o1asXxo4di/Dw8HxPITQyMsKKFSsQEhIiuU5UVBT27duHP//8UyjO3t4erVu3llyXiEgpYWFheqlbs2ZNfPDBB3qpTVQUWVtbIzg4WCgmLy8PBw4c0Pj6s2fPIisrS6hG06ZNha4nIqLiz9/fH2ZmZorXkXIwQH6Sk5ORlJQkHKeLQQsiIqLiigMIRERERYSzszPc3NyEYs6fP4+bN2/m+9qGDRsk9TFv3jzY2dlJiiUiIiL5VK5cGcuWLdMqR5MmTdCzZ0+ZOiIiIiLSDUtLS2zZsgU7d+6UFH/u3DlMnDhROK5bt24wNTWVVJOISElubm7w8PDQed2WLVvqvCZRURcUFCQcs3v3bo2vTUxMFM7fqFEj4RgiIirezMzMUL9+fcXryDWAsGjRIuGYihUrolOnTrLUJyIiKok4gEBERFSESDllr0qVKjhx4sRbP8vJycGmTZuEczVp0gS9e/cWjiMiIiJ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XlVI2rHGtHmtjjzkBMCdLN4XdP8m4tw/skeRzpZS3TNqIUEq5WynlHUk+mfFPtf1ekoNqrRdNGvN19FgXe8wJgL70eM1ZTsNxb8CCWOuPbABM106N8YtnEsVon23HjLfiBIDNSinlNhk9mWmcK5L87Rq2maR+zqLWt/ZYSZ3vMScAFkApZackf7fM8JVJHl9rbb1md9Z6PNP3mBMAs7dLY/x7pZQ9knxsgrmrVZI8M8nupZSHruEmux5rY485ATBHtdbflFL+JMkrkzxpmWlbJnl0kkeXUr6f5FNJzktyYUY3qm+X0VN290hyryS3mGDrLyZ5cK31/DWE32Nd7DEnAPrS4zVnOQ3HvQELQgMCwGLZsTE+y8P/uNcRKdQAdKOUsi7JWzO6oDHOa2qt31rDVq06f8nS06GGNs0DeY85AbAYXpXk5suMvbrW+uVZBjOhHs/0PeYEwAyVUrZKcuPGtHVJPpzhmg+u7X5J3ltKuf8qz6s91sYecwJgzmqtlyY5vpRyWkYP/7njmOm3TPLgNWx3eZLXJnn+FJ7k32Nd7DEnAPrS4zVnOQ3HvQELYt28AwDgd2zVGL9kJlEkv26Mt+IEgM3JC5Mc0Jjz3SR/s8Z9eqzzPeYEwJyVUg5Octwywxck+evZRbMiPdbFHnMCYLZ2mGDO65LsOmb8l0n+Ockjk9w5o0aFjUv/vfPS3//z0rxJHJnkFRPOva4ea2OPOQGwIGqtH0pypyQPTHJakrU2CFzbxUlen+T2tda/mELzQdJnXewxJwD60mOtktNwfKdYEN6AALBYtmyMXzmTKNr7tOIEgM1CKeVPkzyvMa0meWytddIbGZbTY53vMScA5qiUcqMkbxgz5fha66x+xF6pHutijzkBMFtbTzDnPsv8/ZVJTkzyV7XWizYx/sOlP19K8k+llB2SnJDkiWlfA316KeXMWusZE8R3bT3Wxh5zAmCB1FprkveXUr6e5L8n+YuMmglX64okL0vyoqU3LUxTj3Wxx5wA6EuPtUpOw/GdYkF4AwLAYlGoAWBGSil7JnlX2ueiv6+1fmQKW/ZY53vMCYD5OiHJ7y8z9t5a62mzDGaFeqyLPeYEwGyt9qlzP0tyr1rr05ZpPrieWutFtdanJrl3kp9P8JGTSikrja/H2thjTgAsiFLKhlLKcaWUc5N8Lcnzs7bmgyTZYmmd80spJ5ZSbr/WOK+lx7rYY04A9KXHWiWn4fhOsSA0IAAslta/y1fNJIr2PutnEgUADKSUcrMkpyTZvjH18xk9jWkaeqzzPeYEwJyUUu6S5OnLDF+c5GkzDGc1eqyLPeYEwGxtsYrP/DjJAbXWz61mw1rr2UkOWFpnnFsnefIKl++xNvaYEwALoJRyZJLzkrw1yZ4DbLFzRm8++kYp5Z2llN+bwpo91sUecwKgLz3WKjkNx3eKBaEBAWCxtDr0Wq+NnpbWPlfMJAoAGEApZZskp2b5pytf42dJHlxrvXxKW/dY53vMCYA5KKVsSPKmLP/D8PNqrd+fYUir0WNd7DEnAGZrNRefj6u1fnUtm9Zav5LkuAmm/t9L30Mm1WNt7DEnAOaolLJ1KeXEjB4CdNsZbLk+ycOTfLmU8rA1rtVjXewxJwD60mOtktNwfKdYEBoQABZL6wbHWRXq1lOppnUjJgDMVCllyyTvS7JvY+pvktyv1nrBFLfvsc73mBMA8/EXSfZeZuxzSV43w1hWq8e62GNOAMzWSv+NPqnWesY0Nl5a502NabdMcvQKlu2xNvaYEwBzUkrZOqMHAD1xgulXJTkzyfMzenvRHZLslFFNuFmSPZIcluRFST45wXo3TvKuUsr/WHHgv9VjXewxJwD60mOtktNwfKdYELP6fzgAk2l14G05kygUagA6VEpZl+SfMrpgMc4VGb354FNTDqHHOt9jTgDMWCnl9klesMzwlUmeUGu9eoYhrVaPdbHHnACYrZXWnb+a8v4vSPKojL8m+sAk759wvR5rY485ATAHSw8A+mCSAxtTr0jy+iSvqrV+Z5k5P136880kH15a/45Jnp3kEUnKmPWfW0q5ota6mu8VPdbFHnMCoC891io5Dcd3igXhDQgAi+VXjfHtZhJFsn1jvBUnACyUUkpJclKSBzemXp3kuFrraQOE0aqf2w6w56ZMs873mBMAs3dSkq2XGfu7WuuXZhnMGvR4pu8xJwBm69crmHtyrfWH09y81vqDJCc3pv3J0kMLJtFjbewxJwDm44VJDm7MuSDJfWqtTx3TfLBJtdav1FqPTXK/JBc2pr+glPLAlay/pMe62GNOAPSlx2vOchqO7xQLQgMCwGL5eWN8Vof/1j6tOAFg0bw6yWMnmPfEWuu7BoqhVT+3KKVsNdDe1zbNOt9jTgDMUCnlsUn+eJnhCzL9pyAPqcczfY85ATBbFyapE85960Ax/ENjfKcke0y4Vo+1scecAJixUso9kzyrMe28JHettZ69lr1qrack2S/JzxpTX1dKufkKl++xLvaYEwB96fGas5yG4zvFgtCAALBYWj+S7DCTKJIbN8ZbcQLAwiilvCjJ0yaY+oxa60kDhjJJ/ZxFrW/tsZI632NOAMxIKeUWSV4+ZsqTa60reWryvPV4pu8xJwBmqNZ6VZJfTDI1yacHCuMzaTdB3GXCtXqsjT3mBMDsvTTj70G6MMkRtdafTmOzWus3kzwgyeVjpt0syQtWuHSPdbHHnADoS4/XnOU0HPcGLAgNCACLpfWDy8ZSyqCFupRykyRbNqYp1ABsFkopz07y/Amm/lWt9VUDhzPJhZWdB44hSXZpjK+kzveYEwCz8/dJdlxm7H211lNnGcwU9Him7zEnAGZvkrPjf9RaLxpi81rrhRk9cXmc3SZcrsfa2GNOAMxQKeWuSe7TmPZXtdZWPV6RWusnkry+Me1RK6xjPdbFHnMCoC89XnOW03DcG7AgNCAALJb/mmDOLQaOYZL1J4kTAOaqlPLUjJ661PLyWusJQ8dTa70k7cPuoHW+lHKjJNs2pl0w6Xo95gTAbJRSjk7yoGWGL07y1BmGMy09nul7zAmA2Zvk3+mvDRxDa/1bT7hOj7Wxx5wAmK3HNsa/m2Sotw+/OMm4tyduk+SRK1ivx7rYY04AdKTHa85yGoZ7AxaLBgSABVJr/VXahfo2A4fRWv/HtdZxP+IAwNyVUv48yWsmmPo/a63PGjqea/lOY3zedT5px7jS+ZtjTgAMb9ybh/6y1vr9mUUyJT2e6XvMCYC5OH+COYO8/eBaLmyM7zTJIj3Wxh5zAmDm/rgx/u5a62VDbFxr/XGSMxvTDlzBet3VxR5zAqBL32mMz7tWJa6jTzJ/c8yJVdow7wAAuJ7zk9xkzPgdknx4wP3v0Bif5GIRAMxNKeWRGb12uTSmvjnJU4aP6Hecn+QuY8ZbdXitWuv/aOnJBSvRY04ADO+my/z9xUkuK6U8bop77dMYv8ME+/1rrfW8Cfbq8UzfY04AzNa3J5gzdANCa/0brWCtHmtjjzkBMAOllJsn2aMxbcgacs36x4wZv3cppdRa64Tr9VgXe8wJgL70eM1ZTtPn3oAFogEBYPGcm+SuY8ZbP+Cs1e6N8XMH3h8AVq2U8uAk/5D2297eleTxK7jgMC3nJnnQmPHNsc73mBMA87N9kjfMeM97Lv0Z59FJJmlA6PFM32NOAMzWVyeY85uBY2itv5Jrpj3Wxh5zAmA2bjfBnM8NHENr/ZtmdPP9Tydcr8e62GNOAPSlx2vOcpo+3ykWSOumHABm798b43ceeP/WkyG/OPD+ALAqpZSjk7wjyfrG1JOT/Fmt9erho7qeHut8jzkBwGr1WBd7zAmA2fq3CebceOAYWuuvpAGix9rYY04AzMa4p+onyeW11l8MHMOPJ5jTivPaeqyLPeYEQF96rFVymj7fKRaIBgSAxdMq1HuXUlo3Vq5KKWVDkjs1pinUACycUsphSd6TZIvG1NOTPLTWeuXwUW1Sq87vuvTK6qGMex1iMsyPDJtjTgCwWj2e6XvMCYAZqrV+L8mPGtN2GDiMHRvjv1rBWj3Wxh5zAmA2WjX2ZzOIYZI3G+y0gvV6rIs95gRAX3q85iyn6XNvwALRgACweL6Q5NIx49umXUxXa98kNxozfmkme1oVAMxMKeWAJP8rycbG1LOSHFNrvXzwoJZRa/3fSS5oTDtgiL1LKbdM+5WEn1zpuj3mBABr0OOZvsecAJi91tlsyIvTk6z/vRWs1WNt7DEnAGbjqsZ463f7aZhkj7qC9Xqsiz3mBEBHerzmLKfpcm/A4tGAALBgaq2XJvlUY9ohA21/cGP8E0vxAcBCKKXcI8kpSbZuTP1kkqMXpI59tDE+rzp/Xq219WPBcnrMCQBWrMczfY85ATAXZzbG7zrUxqWUkmSfxrSJz4491sYecwJgZn7dGN9xqCfrX8vNJphzyaSL9VgXe8wJgP+/vbuPub+s6wD+vigsyAekBzUV0BUwjKUYPkQqAgqUy5W6mknorI2ZVmZLrWmAVtLTYANdbTnQWZSyNCMFdAaEpZSCTWWhoojyJELAeEjg0x/ndpH6O9c55z7n3Pd98Xptv7++131d1+evz+97ru/7+x3SiGfOaloezwZsMwIIANvTBZ3rP7+idV/YuX7+itYFgLm11g5J8oFM3swzzaVJfqaqegch69Lr8z+7ogOZVfb5EWsCgEWNeE8/Yk0ArFcvgLB3a633FrtF7Z9k786Yy+ecc8TeOGJNAKzedZ3rLcmjV7yHx84w5vo55xyxL45YEwBjGfHMWU3L4/8U24wAAsD29J7O9UNaawcsc8HW2o8lObgz7JxlrgkAi9roW+cneVhn6OVJjq6qW1e/q5mdm+lvW/qh9NP7c2mt7Z3k6M6wd29iiRFrAmCFqmqvqmrr+JfkpM52zpphnjPnKG/Ee/oRawJgjarq6iT/1hn23BUt35v33iT/MeecI/bGEWsCYPW+MMOYZ694D0d2rt9ZVfMGEEbsiyPWBMBYRjxzVtMSeDZgexJAANiGqurz6R/GvGrJy/565/olVXXVktcEgLltvBHxQ0m+vzP0M0meU1U3r35Xs6uq25P8Q2fYsvv8CUkeNOX6NUkuWnTyEWsCgEWNeE8/Yk0AbIl3dK6fsKJ1e/NeMu9XE0fsjSPWBMDqVdVNSb7SGXbMirdxbOf6p+adcMS+OGJNAIxlxDNnNS2NZwO2IQEEgO3r7Z3rL2utPWoZC7XWHpPkuM6wM5exFgBsRmttvyQfTvKIztArkxxVVTeuek8L6vX5n26tPXEZC7XWHpz+Df5ZVVWbXGrEmgBgUSPe049YEwDrdXaSaQ/6P6G1dsQyF2ytHZnkoM6wv19w+hF744g1AbB6H+1cf8HGb/tL11p7dpInd4b19rcrI/bFEWsCYCwjnjmraRM8G7B9CSAAbF/vTHLDlOt7JnnLktY6Jcn3Trl+/cZ+AGDLtNZ+OJPwwWM6Q7+Y5Miqunblm1pQVV2Q6W9daklOXdJyr0/yyCnX705y+mYXGbEmANiEEe/pR6wJgDXa+ELhX3aGndFam9YDZrYxzxmdYXdl8Z4yYm8csSYAVq/3Btzdk5y87EVba7sl+aMZhv7jgkuM2BdHrAmAgYx45qymTfNswDYlgACwTVXVXUlO6wz75dbaz21mndbai5K8uDPs1Kq6ezPrAMBmtNZ+MMmHkjy+M/SaJEdU1ZdXv6tNO6Vz/VmttVdvZoHW2tOT/E5n2JlVdd1m1rmfEWsCgLmNeE8/Yk0AbIk/zeRgeFcOzPIeentLkgM6Y95ZVTctMvmIvXHEmgBYi/club0z5rjW2q8sed0/S/LUzphrk1y4yOQj9sURawJgSCOeOatpAZ4N2N6ar04AbF+ttT2TfDbJPlOG3ZbkqKr6+ALzPy3JBUkePGXY1UkOrKo7550fAJahtbZXko8k6X2i77okz6yqK1e/q81rrbUkH0ty6JRh30jygqp6/wLz/2gmByvTPpd8W5L9l3VDPmJNAOx8rbUTk/z+lCFnVdVLV7DucPf0I9YEwPq11k5O8obOsBOr6qRNrHFipvf/JLkjk/vHr2xineF644g1AbB6rbVTk/xGZ9g3kry4qt6zhPV+L8mbZxj6uqrqPRw3bZ3h+uKINQGwmNba4Zmcw+/Kl6pqv/Xs5v+MeOaspp1RE/PxBQSAbayq7kjyms6whyQ5v7X2vHnmbq09P8l5mX7jnyS/5cYfgK3SWntwkn9KP3zwtUx+DN8R4YMkqUka/JVJpqXCd0/y7nnfDNVaOyz9m/EkOWmZN+Mj1gQAixrxnn7EmgDYEn+Y5POdMSe21t7aWnvIPBO31h7aWntb+uGDJHnzZsIHyZi9ccSaAFiLU5Lc2hnzzd+GT2ut7bHIIq21H2itvS+zhQ++muSti6zzTSP2xRFrAmAsI545q2ln1MR8fAEBYAdorb0r/U8UVpK/SfKmqrpiylwHJXljkl+YYel3VdVLZt4oACxZa+39SWb5gfuMJJeteDv3d21VnbuMiVprf5Dkd2cY+sEkb6yqS6fMtW+S1yb51STf3ZnvwiRHVtW9s+51ViPWBMDOtVVfQLjf+sPd049YEwDr1Vp7SpKLkzyoM/S6JCcm+buqunnKfHsneVGSk5I8YoYtXJzk8Kq6b6YNd4zYG0esCYDVaq29IpPf6mdxY5LTk/zVLIHA1tqBSV6R5OVJ9pxxjRdW1Tkzju2tP1xfHLEmgAey1tozk+w/558dkOS3p1y/KcnrFtjOhct4ceCIZ85q2hk1MRsBBIAdYOPtz5cmOXDGP/lkko8muSrJ7Zm8oeBxSQ5L8uMzznFFkkOr6vb5dgsAy9Na+2KSfbd6H9/BhVV1+DImaq19V5IPJ3nWjH9yRSYPSlyZyRulvi/JY5M8NcnTkrQZ5rghyZOq6qtzb3gGI9YEwM61DQIIw93Tj1gTAOvXWjshydtmHH5Pkn9J8p+ZhBJuTvLwJI9McnCSn0r/YPqbrkry9Kq6fq4NTzFibxyxJgBWr7V2dmZ7CP3+Pp/kkiRfSfL1TPrIw5LsnWS/JM9I/+233+q0qvrNOf9ml0bsiyPWBPBA1lo7OBbWcgAACBhJREFUM8nxW72PDS+rqjM3O8mIZ85qSrIDamI2AggAO0RrbZ9Mmu8+a1ju6iTPqKqr17AWAOzSAyGAkCSttb2SfCTJE5c15xS3JHl2Va30ixEj1gTAzrTVAYSNPQx3Tz9iTQCsX2vtDUlOXuOS12Tydrz/WvbEI/bGEWsCYLVaa3skeW+S527hNs5OclxV3bPMSUfsiyPWBPBANWIAIRnzzFlNm+bZgG1it63eAACz2bgRPzKTN0Cs0ueSHOHGHwDWp6puyeRA5t9XvNQNSY5ex834iDUBwKJGvKcfsSYA1q+q3pTktUnuW8Nyn05y2CrCB8mYvXHEmgBYraq6M8nzk/z1Fm3h9CQvWXb4IBmzL45YEwBjGfHMWU2b4tmAbUQAAWAHqarPJTk0yXkrWuKDSZ5SVav+gQEA+BZVdWMmn5J+x4qWuDTJT1TVx1c0/7cZsSYAWNSI9/Qj1gTA+lXVHyc5NsnXVrjM2zPpKSt96G3E3jhiTQCsVlXdVVW/lOSEJP+9pmVvSPKLVfWqqrp3VYuM2BdHrAmAsYx45qymhXg2YJsRQADYYarq5qo6JslLM/khZRluSHJ8VR1bVTcvaU4AYE4bBzPHJ3leki8sadrbkrwmyU9W1ZeXNOfMRqwJABY14j39iDUBsH5VdX6SA5KckWSZDw1+IsmzqurlVXXHEufdpRF744g1AbB6VfUXSQ7MpL/ftaJlbk1ySpIDqupvV7TG/zNiXxyxJgDGMuKZs5pm5tmAbUoAAWCHqqqzkjw+ya8l+eyC03xm4+8fV1WrSh8CAHOqqnMzOZg5LpMk/yK+lOT1Sfarqj9fxSen5zFiTQCwqBHv6UesCYD1qqqvV9Urk/xIkj/J4g+/3ZHknCTPqaonV9VFy9rjPEbsjSPWBMBqVdV1G/193ySvTvKxJPdtctp7klyUyRcW9qmq11XVLZucc24j9sURawJgLCOeOatpl7ZVTXy7VlVbvQcAlqC1tn+SY5IckuQJSR6d5CFJ9szkwOW2JNdkcsP/iSQfqKort2a3AMA8WmuPTXJsJp9BPiiTw5qHZtLn786kz1+byYHAZUnOq6rLt2a3sxmxJgC2p9ba4UkOnzLksqp673p2852NeE8/Yk0ArFdrbbckT05yVJKDMzm4flQm/WSPJP+T5PZM7h2vSvKpJP+a5J/X9bWDeYzYG0esCYDVa609PMkzkzwpk/6xTyY9fq8k35Nk90z6/F1Jbs6k138xyaeTfDLJxVV129o33jFiXxyxJgDGMuKZs5p2Rk0IIAAAAAAAAAAAAAAAADPYbas3AAAAAAAAAAAAAAAAbH8CCAAAAAAAAAAAAAAAQJcAAgAAAAAAAAAAAAAA0CWAAAAAAAAAAAAAAAAAdAkgAAAAAAAAAAAAAAAAXQIIAAAAAAAAAAAAAABAlwACAAAAAAAAAAAAAADQJYAAAAAAAAAAAAAAAAB0CSAAAAAAAAAAAAAAAABdAggAAAAAAAAAAAAAAECXAAIAAAAAAAAAAAAAANAlgAAAAAAAAAAAAAAAAHQJIAAAAAAAAAAAAAAAAF0CCAAAAAAAAAAAAAAAQJcAAgAAAAAAAAAAAAAA0CWAAAAAAAAAAAAAAAAAdAkgAAAAAAAAAAAAAAAAXQIIAAAAAAAAAAAAAABAlwACAAAAAAAAAAAAAADQJYAAAAAAAAAAAAAAAAB0CSAAAAAAAAAAAAAAAABdAggAAAAAAAAAAAAAAECXAAIAAAAAAAAAAAAAANAlgAAAAAAAAAAAAAAAAHQJIAAAAAAAAAAAAAAAAF0CCAAAAAAAAAAAAAAAQJcAAgAAAAAAAAAAAAAA0CWAAAAAAAAAAAAAAAAAdAkgAAAAAAAAAAAAAAAAXQIIAAAAAAAAAAAAAABAlwACAAAAAAAAAAAAAADQJYAAAAAAAAAAAAAAAAB0CSAAAAAAAAAAAAAAAABdAggAAAAAAAAAAAAAAECXAAIAAAAAAAAAAAAAANAlgAAAAAAAAAAAAAAAAHQJIAAAAAAAAAAAAAAAAF0CCAAAAAAAAAAAAAAAQJcAAgAAAAAAAAAAAAAA0CWAAAAAAAAAAAAAAAAAdAkgAAAAAAAAAAAAAAAAXQIIAAAAAAAAAAAAAABAlwACAAAAAAAAAAAAAADQJYAAAAAAAAAAAAAAAAB0CSAAAAAAAAAAAAAAAABdAggAAAAAAAAAAAAAAECXAAIAAAAAAAAAAAAAANAlgAAAAAAAAAAAAAAAAHQJIAAAAAAAAAAAAAAAAF0CCAAAAAAAAAAAAAAAQJcAAgAAAAAAAAAAAAAA0CWAAAAAAAAAAAAAAAAAdAkgAAAAAAAAAAAAAAAAXQIIAAAAAAAAAAAAAABAlwACAAAAAAAAAAAAAADQJYAAAAAAAAAAAAAAAAB0CSAAAAAAAAAAAAAAAABdAggAAAAAAAAAAAAAAECXAAIAAAAAAAAAAAAAANAlgAAAAAAAAAAAAAAAAHQJIAAAAAAAAAAAAAAAAF0CCAAAAAAAAAAAAAAAQJcAAgAAAAAAAAAAAAAA0CWAAAAAAAAAAAAAAAAAdAkgAAAAAAAAAAAAAAAAXQIIAAAAAAAAAAAAAABAlwACAAAAAAAAAAAAAADQJYAAAAAAAAAAAAAAAAB0CSAAAAAAAAAAAAAAAABdAggAAAAAAAAAAAAAAECXAAIAAAAAAAAAAAAAANAlgAAAAAAAAAAAAAAAAHQJIAAAAAAAAAAAAAAAAF0CCAAAAAAAAAAAAAAAQNf/AhMwcLy+FPNlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_autocorrelation(samples[:,1,0], 'k', 't')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
